"""Unit tests for run_agent.py (AIAgent).
Tests cover pure functions, state/structure methods, and conversation loop
pieces. The OpenAI client and tool loading are mocked so no network calls
are made.
"""
import io
import json
import logging
import re
import uuid
from logging.handlers import RotatingFileHandler
from pathlib import Path
from types import SimpleNamespace
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from agent.codex_responses_adapter import _chat_messages_to_responses_input, _normalize_codex_response, _preflight_codex_input_items
import run_agent
from run_agent import AIAgent
from agent.error_classifier import FailoverReason
from agent.prompt_builder import DEFAULT_AGENT_IDENTITY
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
def _make_tool_defs(*names: str) -> list:
"""Build minimal tool definition list accepted by AIAgent.__init__."""
return [
{
"type": "function",
"function": {
"name": n,
"description": f"{n} tool",
"parameters": {"type": "object", "properties": {}},
},
}
for n in names
]
def test_is_destructive_command_treats_cp_as_mutating():
assert run_agent._is_destructive_command("cp .env.local .env") is True
def test_is_destructive_command_treats_install_as_mutating():
assert run_agent._is_destructive_command("install template.env .env") is True
@pytest.fixture()
def agent():
"""Minimal AIAgent with mocked OpenAI client and tool loading."""
with (
patch(
"run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")
),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-key-1234567890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
a.client = MagicMock()
return a
@pytest.fixture()
def agent_with_memory_tool():
"""Agent whose valid_tool_names includes 'memory'."""
with (
patch(
"run_agent.get_tool_definitions",
return_value=_make_tool_defs("web_search", "memory"),
),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-k...7890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
a.client = MagicMock()
return a
def test_aiagent_reuses_existing_errors_log_handler():
"""Repeated AIAgent init should not accumulate duplicate errors.log handlers."""
root_logger = logging.getLogger()
original_handlers = list(root_logger.handlers)
error_log_path = (run_agent._hermes_home / "logs" / "errors.log").resolve()
try:
for handler in list(root_logger.handlers):
root_logger.removeHandler(handler)
error_log_path.parent.mkdir(parents=True, exist_ok=True)
preexisting_handler = RotatingFileHandler(
error_log_path,
maxBytes=2 * 1024 * 1024,
backupCount=2,
)
root_logger.addHandler(preexisting_handler)
with (
patch(
"run_agent.get_tool_definitions",
return_value=_make_tool_defs("web_search"),
),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
AIAgent(
api_key="test-k...7890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
AIAgent(
api_key="test-k...7890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
matching_handlers = [
handler for handler in root_logger.handlers
if isinstance(handler, RotatingFileHandler)
and error_log_path == Path(handler.baseFilename).resolve()
]
assert len(matching_handlers) == 1
finally:
for handler in list(root_logger.handlers):
root_logger.removeHandler(handler)
if handler not in original_handlers:
handler.close()
for handler in original_handlers:
root_logger.addHandler(handler)
class TestProviderModelNormalization:
def test_aiagent_strips_matching_native_provider_prefix(self):
with (
patch(
"run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")
),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
agent = AIAgent(
model="zai/glm-5.1",
provider="zai",
base_url="https://api.z.ai/api/paas/v4",
api_key="test-key-1234567890",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert agent.model == "glm-5.1"
def test_aiagent_keeps_aggregator_vendor_slug(self):
with (
patch(
"run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")
),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
agent = AIAgent(
model="anthropic/claude-sonnet-4.6",
provider="openrouter",
base_url="https://openrouter.ai/api/v1",
api_key="test-key-1234567890",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert agent.model == "anthropic/claude-sonnet-4.6"
# ---------------------------------------------------------------------------
# Helper to build mock assistant messages (API response objects)
# ---------------------------------------------------------------------------
def _mock_assistant_msg(
content="Hello",
tool_calls=None,
reasoning=None,
reasoning_content=None,
reasoning_details=None,
):
"""Return a SimpleNamespace mimicking an OpenAI ChatCompletionMessage."""
msg = SimpleNamespace(content=content, tool_calls=tool_calls)
if reasoning is not None:
msg.reasoning = reasoning
if reasoning_content is not None:
msg.reasoning_content = reasoning_content
if reasoning_details is not None:
msg.reasoning_details = reasoning_details
return msg
def _mock_tool_call(name="web_search", arguments="{}", call_id=None):
"""Return a SimpleNamespace mimicking a tool call object."""
return SimpleNamespace(
id=call_id or f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=SimpleNamespace(name=name, arguments=arguments),
)
def _mock_response(
content="Hello",
finish_reason="stop",
tool_calls=None,
reasoning=None,
reasoning_content=None,
reasoning_details=None,
usage=None,
):
"""Return a SimpleNamespace mimicking an OpenAI ChatCompletion response."""
msg = _mock_assistant_msg(
content=content,
tool_calls=tool_calls,
reasoning=reasoning,
reasoning_content=reasoning_content,
reasoning_details=reasoning_details,
)
choice = SimpleNamespace(message=msg, finish_reason=finish_reason)
resp = SimpleNamespace(choices=[choice], model="test/model")
if usage:
resp.usage = SimpleNamespace(**usage)
else:
resp.usage = None
return resp
# ===================================================================
# Group 1: Pure Functions
# ===================================================================
class TestHasContentAfterThinkBlock:
def test_none_returns_false(self, agent):
assert agent._has_content_after_think_block(None) is False
def test_empty_returns_false(self, agent):
assert agent._has_content_after_think_block("") is False
def test_only_think_block_returns_false(self, agent):
assert agent._has_content_after_think_block("reasoning") is False
def test_content_after_think_returns_true(self, agent):
assert (
agent._has_content_after_think_block("r actual answer")
is True
)
def test_no_think_block_returns_true(self, agent):
assert agent._has_content_after_think_block("just normal content") is True
class TestStripThinkBlocks:
def test_none_returns_empty(self, agent):
assert agent._strip_think_blocks(None) == ""
def test_no_blocks_unchanged(self, agent):
assert agent._strip_think_blocks("hello world") == "hello world"
def test_single_block_removed(self, agent):
result = agent._strip_think_blocks("reasoning answer")
assert "reasoning" not in result
assert "answer" in result
def test_multiline_block_removed(self, agent):
text = "\nline1\nline2\n\nvisible"
result = agent._strip_think_blocks(text)
assert "line1" not in result
assert "visible" in result
def test_orphaned_closing_think_tag(self, agent):
result = agent._strip_think_blocks("some reasoningactual answer")
assert "" not in result
assert "actual answer" in result
def test_orphaned_closing_thinking_tag(self, agent):
result = agent._strip_think_blocks("reasoninganswer")
assert "" not in result
assert "answer" in result
def test_orphaned_opening_think_tag(self, agent):
result = agent._strip_think_blocks("orphaned reasoning without close")
assert "" not in result
def test_mixed_orphaned_and_paired_tags(self, agent):
text = "straypaired reasoning visible"
result = agent._strip_think_blocks(text)
assert "" not in result
assert "" not in result
assert "visible" in result
def test_thought_block_removed(self, agent):
"""Gemma 4 uses tags for inline reasoning."""
result = agent._strip_think_blocks("internal reasoning answer")
assert "internal reasoning" not in result
assert "" not in result
assert "answer" in result
def test_orphaned_thought_tag(self, agent):
result = agent._strip_think_blocks("orphaned reasoning without close")
assert "" not in result
# ─── Unterminated-block coverage (#8878, #9568, #10408) ──────────────
# Reasoning models served via NIM / MiniMax M2.7 frequently drop the
# closing tag, leaking raw reasoning into assistant content. The open
# tag appears at a block boundary (start of text or after a newline);
# everything from that tag to end-of-string is stripped.
def test_unterminated_think_block_content_stripped(self, agent):
"""Content after unterminated is fully stripped."""
result = agent._strip_think_blocks("orphaned reasoning without close")
assert "orphaned reasoning" not in result
assert result.strip() == ""
def test_unterminated_thought_block_content_stripped(self, agent):
"""Gemma-style with no close is fully stripped."""
result = agent._strip_think_blocks("orphaned reasoning without close")
assert "orphaned reasoning" not in result
assert result.strip() == ""
def test_unterminated_multiline_block_stripped(self, agent):
"""Multi-line unterminated blocks are stripped in full."""
result = agent._strip_think_blocks(
"\nmulti\nline\nreasoning\nthat never closes"
)
assert "multi" not in result
assert "never closes" not in result
def test_unterminated_block_after_answer_preserves_prefix(self, agent):
"""Visible answer before a line-starting unterminated tag is kept."""
result = agent._strip_think_blocks(
"Answer is 42.\nactually let me reconsider"
)
assert "Answer is 42." in result
assert "reconsider" not in result
def test_inline_think_mention_in_prose_not_over_stripped(self, agent):
"""Mid-line `` mentioned in prose must not swallow the rest
of the content (the block-boundary check prevents this)."""
text = "Use the tag like this in your prose."
result = agent._strip_think_blocks(text)
# Block-boundary check prevents unterminated-strip from firing
assert "prose" in result
assert "Use the" in result
def test_mixed_case_closed_pair_stripped(self, agent):
"""Mixed-case variants …, … are
handled by case-insensitive closed-pair regex, so the trailing
content is preserved."""
result = agent._strip_think_blocks("upperfinal")
assert "upper" not in result
assert "final" in result
result = agent._strip_think_blocks("mixedfinal")
assert "mixed" not in result
assert "final" in result
# ─── Tool-call XML block stripping (openclaw/openclaw#67318) ─────────
# Some open models (notably Gemma variants via OpenRouter) emit
# standalone tool-call XML inside assistant content instead of via the
# structured `tool_calls` field. Left unstripped, raw XML leaks to
# gateway users (Discord/Telegram/Matrix) and the CLI.
def test_tool_call_block_stripped(self, agent):
text = '{"name": "read_file", "arguments": {"path": "/tmp/x"}} done'
result = agent._strip_think_blocks(text)
assert "" not in result
assert "read_file" not in result
assert "done" in result
def test_function_calls_block_stripped(self, agent):
text = '[{"name":"x"}]after'
result = agent._strip_think_blocks(text)
assert "" not in result
assert "after" in result
def test_gemma_function_name_block_stripped(self, agent):
"""Gemma-style: ...."""
text = (
'Let me check the file.\n'
'/tmp/x.md\n'
'Here is the result.'
)
result = agent._strip_think_blocks(text)
assert '' not in result
assert "/tmp/x.md" not in result
assert "Let me check the file." in result
assert "Here is the result." in result
def test_gemma_function_multiline_payload_stripped(self, agent):
text = (
'Reading now.\n'
'\n'
' /etc/passwd\n'
'\n'
'Done.'
)
result = agent._strip_think_blocks(text)
assert "/etc/passwd" not in result
assert "Reading now." in result
assert "Done." in result
def test_function_mention_in_prose_preserved(self, agent):
"""'Use in JavaScript.' — no name attr, not at block boundary
in a way that suggests tool call. Must survive."""
text = "In JS you can use declarations for hoisting."
result = agent._strip_think_blocks(text)
# Prose mention has no name="..." attribute -> not stripped
assert "declarations for hoisting" in result
def test_function_with_attr_in_middle_of_sentence_preserved(self, agent):
"""Docs example: 'Use ... in docs.'
The sentence-middle position without a preceding punctuation block
boundary means it is NOT stripped. Prose context remains."""
text = 'You can write y inline.'
result = agent._strip_think_blocks(text)
# Without a leading block boundary (no punctuation before), leaves intact
assert "You can write" in result
assert "inline" in result
def test_stray_function_close_tag_removed(self, agent):
text = "answer trailing"
result = agent._strip_think_blocks(text)
assert "" not in result
assert "answer" in result
assert "trailing" in result
def test_dangling_function_open_tag_preserved(self, agent):
"""A streamed-but-truncated block with no close
is intentionally NOT stripped (OpenClaw's asymmetry). The tail of a
streaming reply may still be valuable to the user."""
text = 'Checking: '
result = agent._strip_think_blocks(text)
assert "Checking:" in result
def test_mixed_reasoning_and_tool_call_both_stripped(self, agent):
text = 'let me plan{"name":"x"}final answer'
result = agent._strip_think_blocks(text)
assert "let me plan" not in result
assert "" not in result
assert "final answer" in result
class TestExtractReasoning:
def test_reasoning_field(self, agent):
msg = _mock_assistant_msg(reasoning="thinking hard")
assert agent._extract_reasoning(msg) == "thinking hard"
def test_reasoning_content_field(self, agent):
msg = _mock_assistant_msg(reasoning_content="deep thought")
assert agent._extract_reasoning(msg) == "deep thought"
def test_reasoning_details_array(self, agent):
msg = _mock_assistant_msg(
reasoning_details=[{"summary": "step-by-step analysis"}],
)
assert "step-by-step analysis" in agent._extract_reasoning(msg)
def test_no_reasoning_returns_none(self, agent):
msg = _mock_assistant_msg()
assert agent._extract_reasoning(msg) is None
def test_combined_reasoning(self, agent):
msg = _mock_assistant_msg(
reasoning="part1",
reasoning_content="part2",
)
result = agent._extract_reasoning(msg)
assert "part1" in result
assert "part2" in result
def test_deduplication(self, agent):
msg = _mock_assistant_msg(
reasoning="same text",
reasoning_content="same text",
)
result = agent._extract_reasoning(msg)
assert result == "same text"
@pytest.mark.parametrize(
("content", "expected"),
[
("thinking hard", "thinking hard"),
("step by step", "step by step"),
(
"scratch analysis",
"scratch analysis",
),
],
)
def test_inline_reasoning_blocks_fallback(self, agent, content, expected):
msg = _mock_assistant_msg(content=content)
assert agent._extract_reasoning(msg) == expected
class TestCleanSessionContent:
def test_none_passthrough(self):
assert AIAgent._clean_session_content(None) is None
def test_scratchpad_converted(self):
text = "think answer"
result = AIAgent._clean_session_content(text)
assert "" not in result
assert "" in result
def test_extra_newlines_cleaned(self):
text = "\n\n\nx\n\n\nafter"
result = AIAgent._clean_session_content(text)
# Should not have excessive newlines around think block
assert "\n\n\n" not in result
# Content after think block must be preserved
assert "after" in result
class TestGetMessagesUpToLastAssistant:
def test_empty_list(self, agent):
assert agent._get_messages_up_to_last_assistant([]) == []
def test_no_assistant_returns_copy(self, agent):
msgs = [{"role": "user", "content": "hi"}]
result = agent._get_messages_up_to_last_assistant(msgs)
assert result == msgs
assert result is not msgs # should be a copy
def test_single_assistant(self, agent):
msgs = [
{"role": "user", "content": "hi"},
{"role": "assistant", "content": "hello"},
]
result = agent._get_messages_up_to_last_assistant(msgs)
assert len(result) == 1
assert result[0]["role"] == "user"
def test_multiple_assistants_returns_up_to_last(self, agent):
msgs = [
{"role": "user", "content": "q1"},
{"role": "assistant", "content": "a1"},
{"role": "user", "content": "q2"},
{"role": "assistant", "content": "a2"},
]
result = agent._get_messages_up_to_last_assistant(msgs)
assert len(result) == 3
assert result[-1]["content"] == "q2"
def test_assistant_then_tool_messages(self, agent):
msgs = [
{"role": "user", "content": "do something"},
{"role": "assistant", "content": "ok", "tool_calls": [{"id": "1"}]},
{"role": "tool", "content": "result", "tool_call_id": "1"},
]
# Last assistant is at index 1, so result = msgs[:1]
result = agent._get_messages_up_to_last_assistant(msgs)
assert len(result) == 1
assert result[0]["role"] == "user"
class TestMaskApiKey:
def test_none_returns_none(self, agent):
assert agent._mask_api_key_for_logs(None) is None
def test_short_key_returns_stars(self, agent):
assert agent._mask_api_key_for_logs("short") == "***"
def test_long_key_masked(self, agent):
key = "sk-or-v1-abcdefghijklmnop"
result = agent._mask_api_key_for_logs(key)
assert result.startswith("sk-or-v1")
assert result.endswith("mnop")
assert "..." in result
# ===================================================================
# Group 2: State / Structure Methods
# ===================================================================
class TestInit:
def test_anthropic_base_url_accepted(self):
"""Anthropic base URLs should route to native Anthropic client."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter._anthropic_sdk") as mock_anthropic,
):
agent = AIAgent(
api_key="test-key-1234567890",
base_url="https://api.anthropic.com/v1/",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert agent.api_mode == "anthropic_messages"
mock_anthropic.Anthropic.assert_called_once()
def test_prompt_caching_claude_openrouter(self):
"""Claude model via OpenRouter should enable prompt caching."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-k...7890",
model="anthropic/claude-sonnet-4-20250514",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a._use_prompt_caching is True
def test_prompt_caching_non_claude(self):
"""Non-Claude model should disable prompt caching."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-key-1234567890",
base_url="https://openrouter.ai/api/v1",
model="openai/gpt-4o",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a._use_prompt_caching is False
def test_prompt_caching_non_openrouter(self):
"""Custom base_url (not OpenRouter) should disable prompt caching."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-key-1234567890",
model="anthropic/claude-sonnet-4-20250514",
base_url="http://localhost:8080/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a._use_prompt_caching is False
def test_prompt_caching_native_anthropic(self):
"""Native Anthropic provider should enable prompt caching."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter._anthropic_sdk"),
):
a = AIAgent(
api_key="test-key-1234567890",
base_url="https://api.anthropic.com/v1/",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a.api_mode == "anthropic_messages"
assert a._use_prompt_caching is True
def test_prompt_caching_cache_ttl_defaults_without_config(self):
"""cache_ttl stays 5m when prompt_caching is absent from config."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
patch("hermes_cli.config.load_config", return_value={}),
):
a = AIAgent(
api_key="test-k...7890",
model="anthropic/claude-sonnet-4-20250514",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a._cache_ttl == "5m"
def test_prompt_caching_cache_ttl_custom_1h(self):
"""prompt_caching.cache_ttl 1h is applied when present in config."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
patch(
"hermes_cli.config.load_config",
return_value={"prompt_caching": {"cache_ttl": "1h"}},
),
):
a = AIAgent(
api_key="test-k...7890",
model="anthropic/claude-sonnet-4-20250514",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a._cache_ttl == "1h"
def test_prompt_caching_cache_ttl_invalid_falls_back(self):
"""Non-Anthropic TTL values keep default 5m without raising."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
patch(
"hermes_cli.config.load_config",
return_value={"prompt_caching": {"cache_ttl": "30m"}},
),
):
a = AIAgent(
api_key="test-k...7890",
model="anthropic/claude-sonnet-4-20250514",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a._cache_ttl == "5m"
def test_valid_tool_names_populated(self):
"""valid_tool_names should contain names from loaded tools."""
tools = _make_tool_defs("web_search", "terminal")
with (
patch("run_agent.get_tool_definitions", return_value=tools),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-key-1234567890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert a.valid_tool_names == {"web_search", "terminal"}
def test_session_id_auto_generated(self):
"""Session ID should be auto-generated in YYYYMMDD_HHMMSS_ format."""
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
):
a = AIAgent(
api_key="test-key-1234567890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
# Format: YYYYMMDD_HHMMSS_<6 hex chars>
assert re.match(r"^\d{8}_\d{6}_[0-9a-f]{6}$", a.session_id), (
f"session_id doesn't match expected format: {a.session_id}"
)
class TestInterrupt:
def test_interrupt_sets_flag(self, agent):
with patch("run_agent._set_interrupt"):
agent.interrupt()
assert agent._interrupt_requested is True
def test_interrupt_with_message(self, agent):
with patch("run_agent._set_interrupt"):
agent.interrupt("new question")
assert agent._interrupt_message == "new question"
def test_clear_interrupt(self, agent):
with patch("run_agent._set_interrupt"):
agent.interrupt("msg")
agent.clear_interrupt()
assert agent._interrupt_requested is False
assert agent._interrupt_message is None
def test_is_interrupted_property(self, agent):
assert agent.is_interrupted is False
with patch("run_agent._set_interrupt"):
agent.interrupt()
assert agent.is_interrupted is True
class TestHydrateTodoStore:
def test_no_todo_in_history(self, agent):
history = [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi"},
]
with patch("run_agent._set_interrupt"):
agent._hydrate_todo_store(history)
assert not agent._todo_store.has_items()
def test_recovers_from_history(self, agent):
todos = [{"id": "1", "content": "do thing", "status": "pending"}]
history = [
{"role": "user", "content": "plan"},
{"role": "assistant", "content": "ok"},
{
"role": "tool",
"content": json.dumps({"todos": todos}),
"tool_call_id": "c1",
},
]
with patch("run_agent._set_interrupt"):
agent._hydrate_todo_store(history)
assert agent._todo_store.has_items()
def test_skips_non_todo_tools(self, agent):
history = [
{
"role": "tool",
"content": '{"result": "search done"}',
"tool_call_id": "c1",
},
]
with patch("run_agent._set_interrupt"):
agent._hydrate_todo_store(history)
assert not agent._todo_store.has_items()
def test_invalid_json_skipped(self, agent):
history = [
{
"role": "tool",
"content": 'not valid json "todos" oops',
"tool_call_id": "c1",
},
]
with patch("run_agent._set_interrupt"):
agent._hydrate_todo_store(history)
assert not agent._todo_store.has_items()
class TestBuildSystemPrompt:
def test_always_has_identity(self, agent):
prompt = agent._build_system_prompt()
assert DEFAULT_AGENT_IDENTITY in prompt
def test_can_use_soul_identity_even_when_context_files_are_skipped(self):
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("terminal")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
patch("run_agent.load_soul_md", return_value="SOUL IDENTITY"),
):
agent = AIAgent(
api_key="test-k...7890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
load_soul_identity=True,
skip_memory=True,
)
prompt = agent._build_system_prompt()
assert "SOUL IDENTITY" in prompt
assert DEFAULT_AGENT_IDENTITY not in prompt
def test_includes_system_message(self, agent):
prompt = agent._build_system_prompt(system_message="Custom instruction")
assert "Custom instruction" in prompt
def test_memory_guidance_when_memory_tool_loaded(self, agent_with_memory_tool):
from agent.prompt_builder import MEMORY_GUIDANCE
prompt = agent_with_memory_tool._build_system_prompt()
assert MEMORY_GUIDANCE in prompt
def test_no_memory_guidance_without_tool(self, agent):
from agent.prompt_builder import MEMORY_GUIDANCE
prompt = agent._build_system_prompt()
assert MEMORY_GUIDANCE not in prompt
def test_includes_datetime(self, agent):
prompt = agent._build_system_prompt()
# Should contain current date info like "Conversation started:"
assert "Conversation started:" in prompt
def test_includes_nous_subscription_prompt(self, agent, monkeypatch):
monkeypatch.setattr(run_agent, "build_nous_subscription_prompt", lambda tool_names: "NOUS SUBSCRIPTION BLOCK")
prompt = agent._build_system_prompt()
assert "NOUS SUBSCRIPTION BLOCK" in prompt
def test_skills_prompt_derives_available_toolsets_from_loaded_tools(self):
tools = _make_tool_defs("web_search", "skills_list", "skill_view", "skill_manage")
toolset_map = {
"web_search": "web",
"skills_list": "skills",
"skill_view": "skills",
"skill_manage": "skills",
}
with (
patch("run_agent.get_tool_definitions", return_value=tools),
patch(
"run_agent.check_toolset_requirements",
side_effect=AssertionError("should not re-check toolset requirements"),
),
patch("run_agent.get_toolset_for_tool", create=True, side_effect=toolset_map.get),
patch("run_agent.build_skills_system_prompt", return_value="SKILLS_PROMPT") as mock_skills,
patch("run_agent.OpenAI"),
):
agent = AIAgent(
api_key="test-k...7890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
prompt = agent._build_system_prompt()
assert "SKILLS_PROMPT" in prompt
assert mock_skills.call_args.kwargs["available_tools"] == set(toolset_map)
assert mock_skills.call_args.kwargs["available_toolsets"] == {"web", "skills"}
class TestToolUseEnforcementConfig:
"""Tests for the agent.tool_use_enforcement config option."""
def _make_agent(self, model="openai/gpt-4.1", tool_use_enforcement="auto"):
"""Create an agent with tools and a specific enforcement config."""
with (
patch(
"run_agent.get_tool_definitions",
return_value=_make_tool_defs("terminal", "web_search"),
),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
patch(
"hermes_cli.config.load_config",
return_value={"agent": {"tool_use_enforcement": tool_use_enforcement}},
),
):
a = AIAgent(
model=model,
api_key="test-key-1234567890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
a.client = MagicMock()
return a
def test_auto_injects_for_gpt(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="openai/gpt-4.1", tool_use_enforcement="auto")
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_auto_injects_for_codex(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="openai/codex-mini", tool_use_enforcement="auto")
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_auto_skips_for_claude(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="anthropic/claude-sonnet-4", tool_use_enforcement="auto")
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE not in prompt
def test_true_forces_for_all_models(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="anthropic/claude-sonnet-4", tool_use_enforcement=True)
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_string_true_forces_for_all_models(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="anthropic/claude-sonnet-4", tool_use_enforcement="true")
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_always_forces_for_all_models(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="deepseek/deepseek-r1", tool_use_enforcement="always")
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_false_disables_for_gpt(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="openai/gpt-4.1", tool_use_enforcement=False)
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE not in prompt
def test_string_false_disables(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(model="openai/gpt-4.1", tool_use_enforcement="off")
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE not in prompt
def test_custom_list_matches(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(
model="deepseek/deepseek-r1",
tool_use_enforcement=["deepseek", "gemini"],
)
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_custom_list_no_match(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(
model="anthropic/claude-sonnet-4",
tool_use_enforcement=["deepseek", "gemini"],
)
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE not in prompt
def test_custom_list_case_insensitive(self):
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
agent = self._make_agent(
model="openai/GPT-4.1",
tool_use_enforcement=["GPT", "Codex"],
)
prompt = agent._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE in prompt
def test_no_tools_never_injects(self):
"""Even with enforcement=true, no injection when agent has no tools."""
from agent.prompt_builder import TOOL_USE_ENFORCEMENT_GUIDANCE
with (
patch("run_agent.get_tool_definitions", return_value=[]),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI"),
patch(
"hermes_cli.config.load_config",
return_value={"agent": {"tool_use_enforcement": True}},
),
):
a = AIAgent(
api_key="test-key-1234567890",
base_url="https://openrouter.ai/api/v1",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
enabled_toolsets=[],
)
a.client = MagicMock()
prompt = a._build_system_prompt()
assert TOOL_USE_ENFORCEMENT_GUIDANCE not in prompt
class TestInvalidateSystemPrompt:
def test_clears_cache(self, agent):
agent._cached_system_prompt = "cached value"
agent._invalidate_system_prompt()
assert agent._cached_system_prompt is None
def test_reloads_memory_store(self, agent):
mock_store = MagicMock()
agent._memory_store = mock_store
agent._cached_system_prompt = "cached"
agent._invalidate_system_prompt()
mock_store.load_from_disk.assert_called_once()
class TestBuildApiKwargs:
def test_basic_kwargs(self, agent):
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["model"] == agent.model
assert kwargs["messages"] is messages
assert kwargs["timeout"] == 1800.0
def test_public_moonshot_kimi_k2_5_omits_temperature(self, agent):
"""Kimi models should NOT have client-side temperature overrides.
The Kimi gateway selects the correct temperature server-side.
"""
agent.base_url = "https://api.moonshot.ai/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-k2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert "temperature" not in kwargs
def test_public_moonshot_cn_kimi_k2_5_omits_temperature(self, agent):
agent.base_url = "https://api.moonshot.cn/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-k2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert "temperature" not in kwargs
def test_kimi_coding_endpoint_omits_temperature(self, agent):
agent.base_url = "https://api.kimi.com/coding/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-k2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert "temperature" not in kwargs
def test_kimi_coding_endpoint_sends_max_tokens_and_reasoning(self, agent):
"""Kimi endpoint should send max_tokens=32000 and reasoning_effort as
top-level params, matching Kimi CLI's default behavior."""
agent.base_url = "https://api.kimi.com/coding/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-for-coding"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["max_tokens"] == 32000
assert kwargs["reasoning_effort"] == "medium"
def test_kimi_coding_endpoint_respects_custom_effort(self, agent):
"""reasoning_effort should reflect reasoning_config.effort when set."""
agent.base_url = "https://api.kimi.com/coding/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-for-coding"
agent.reasoning_config = {"enabled": True, "effort": "high"}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["reasoning_effort"] == "high"
def test_kimi_coding_endpoint_sends_thinking_extra_body(self, agent):
"""Kimi endpoint should send extra_body.thinking={"type":"enabled"}
to activate reasoning mode, mirroring Kimi CLI's with_thinking()."""
agent.base_url = "https://api.kimi.com/coding/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-for-coding"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["thinking"] == {"type": "enabled"}
def test_kimi_coding_endpoint_disables_thinking(self, agent):
"""When reasoning_config.enabled=False, thinking should be disabled
and reasoning_effort should be omitted entirely — mirroring Kimi
CLI's with_thinking("off") which maps to reasoning_effort=None."""
agent.base_url = "https://api.kimi.com/coding/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-for-coding"
agent.reasoning_config = {"enabled": False}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["thinking"] == {"type": "disabled"}
assert "reasoning_effort" not in kwargs
def test_moonshot_endpoint_sends_max_tokens_and_reasoning(self, agent):
"""api.moonshot.ai should get the same Kimi-compatible params."""
agent.base_url = "https://api.moonshot.ai/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-k2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["max_tokens"] == 32000
assert kwargs["reasoning_effort"] == "medium"
assert kwargs["extra_body"]["thinking"] == {"type": "enabled"}
def test_moonshot_cn_endpoint_sends_max_tokens_and_reasoning(self, agent):
"""api.moonshot.cn (China endpoint) should get the same params."""
agent.base_url = "https://api.moonshot.cn/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "kimi-k2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["max_tokens"] == 32000
assert kwargs["reasoning_effort"] == "medium"
assert kwargs["extra_body"]["thinking"] == {"type": "enabled"}
def test_provider_preferences_injected(self, agent):
agent.base_url = "https://openrouter.ai/api/v1"
agent.providers_allowed = ["Anthropic"]
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["provider"]["only"] == ["Anthropic"]
def test_reasoning_config_default_openrouter(self, agent):
"""Default reasoning config for OpenRouter should be medium."""
agent.base_url = "https://openrouter.ai/api/v1"
agent.model = "anthropic/claude-sonnet-4-20250514"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
reasoning = kwargs["extra_body"]["reasoning"]
assert reasoning["enabled"] is True
assert reasoning["effort"] == "medium"
def test_reasoning_config_custom(self, agent):
agent.base_url = "https://openrouter.ai/api/v1"
agent.model = "anthropic/claude-sonnet-4-20250514"
agent.reasoning_config = {"enabled": False}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["reasoning"] == {"enabled": False}
def test_reasoning_not_sent_for_unsupported_openrouter_model(self, agent):
agent.base_url = "https://openrouter.ai/api/v1"
agent.model = "minimax/minimax-m2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert "reasoning" not in kwargs.get("extra_body", {})
def test_reasoning_sent_for_supported_openrouter_model(self, agent):
agent.base_url = "https://openrouter.ai/api/v1"
agent.model = "qwen/qwen3.5-plus-02-15"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["reasoning"]["effort"] == "medium"
def test_reasoning_sent_for_nous_route(self, agent):
agent.base_url = "https://inference-api.nousresearch.com/v1"
agent.model = "minimax/minimax-m2.5"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["reasoning"]["effort"] == "medium"
def test_reasoning_sent_for_copilot_gpt5(self, agent):
agent.base_url = "https://api.githubcopilot.com"
agent.model = "gpt-5.4"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["reasoning"] == {"effort": "medium"}
def test_reasoning_xhigh_normalized_for_copilot(self, agent):
agent.base_url = "https://api.githubcopilot.com"
agent.model = "gpt-5.4"
agent.reasoning_config = {"enabled": True, "effort": "xhigh"}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["extra_body"]["reasoning"] == {"effort": "high"}
def test_reasoning_omitted_for_non_reasoning_copilot_model(self, agent):
agent.base_url = "https://api.githubcopilot.com"
agent.model = "gpt-4.1"
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert "reasoning" not in kwargs.get("extra_body", {})
def test_max_tokens_injected(self, agent):
agent.max_tokens = 4096
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["max_tokens"] == 4096
def test_qwen_portal_formats_messages_and_metadata(self, agent):
agent.base_url = "https://portal.qwen.ai/v1"
agent._base_url_lower = agent.base_url.lower()
agent.session_id = "sess-123"
messages = [
{"role": "system", "content": "You are helpful"},
{"role": "assistant", "content": "Got it"},
{"role": "user", "content": "hi"},
]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["metadata"]["sessionId"] == "sess-123"
assert kwargs["extra_body"]["vl_high_resolution_images"] is True
assert isinstance(kwargs["messages"][0]["content"], list)
assert kwargs["messages"][0]["content"][0]["cache_control"] == {"type": "ephemeral"}
assert kwargs["messages"][2]["content"][0]["text"] == "hi"
def test_qwen_portal_normalizes_bare_string_content_parts(self, agent):
agent.base_url = "https://portal.qwen.ai/v1"
agent._base_url_lower = agent.base_url.lower()
messages = [
{"role": "system", "content": [{"type": "text", "text": "system"}]},
{"role": "user", "content": ["hello", {"type": "text", "text": "world"}]},
]
kwargs = agent._build_api_kwargs(messages)
user_content = kwargs["messages"][1]["content"]
assert user_content[0] == {"type": "text", "text": "hello"}
assert user_content[1] == {"type": "text", "text": "world"}
def test_qwen_portal_no_system_message(self, agent):
agent.base_url = "https://portal.qwen.ai/v1"
agent._base_url_lower = agent.base_url.lower()
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
# Should not crash even without a system message
assert kwargs["messages"][0]["content"][0]["text"] == "hi"
assert "cache_control" not in kwargs["messages"][0]["content"][0]
def test_qwen_portal_sends_explicit_max_tokens(self, agent):
"""When the user explicitly sets max_tokens, it should be sent to Qwen Portal."""
agent.base_url = "https://portal.qwen.ai/v1"
agent._base_url_lower = agent.base_url.lower()
agent.max_tokens = 4096
messages = [{"role": "system", "content": "sys"}, {"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["max_tokens"] == 4096
def test_qwen_portal_default_max_tokens(self, agent):
"""When max_tokens is None, Qwen Portal gets a default of 65536
to prevent reasoning models from exhausting their output budget."""
agent.base_url = "https://portal.qwen.ai/v1"
agent._base_url_lower = agent.base_url.lower()
agent.max_tokens = None
messages = [{"role": "system", "content": "sys"}, {"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs["max_tokens"] == 65536
def test_ollama_think_false_on_effort_none(self, agent):
"""Custom (Ollama) provider with effort=none should inject think=false."""
agent.provider = "custom"
agent.base_url = "http://localhost:11434/v1"
agent._base_url_lower = agent.base_url.lower()
agent.reasoning_config = {"effort": "none"}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs.get("extra_body", {}).get("think") is False
def test_ollama_think_false_on_enabled_false(self, agent):
"""Custom (Ollama) provider with enabled=false should inject think=false."""
agent.provider = "custom"
agent.base_url = "http://localhost:11434/v1"
agent._base_url_lower = agent.base_url.lower()
agent.reasoning_config = {"enabled": False}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs.get("extra_body", {}).get("think") is False
def test_ollama_no_think_param_when_reasoning_enabled(self, agent):
"""Custom provider with reasoning enabled should NOT inject think=false."""
agent.provider = "custom"
agent.base_url = "http://localhost:11434/v1"
agent._base_url_lower = agent.base_url.lower()
agent.reasoning_config = {"enabled": True, "effort": "medium"}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs.get("extra_body", {}).get("think") is None
def test_non_custom_provider_unaffected(self, agent):
"""OpenRouter provider with effort=none should NOT inject think=false."""
agent.provider = "openrouter"
agent.model = "qwen/qwen3.5-plus-02-15"
agent.reasoning_config = {"effort": "none"}
messages = [{"role": "user", "content": "hi"}]
kwargs = agent._build_api_kwargs(messages)
assert kwargs.get("extra_body", {}).get("think") is None
class TestBuildAssistantMessage:
def test_basic_message(self, agent):
msg = _mock_assistant_msg(content="Hello!")
result = agent._build_assistant_message(msg, "stop")
assert result["role"] == "assistant"
assert result["content"] == "Hello!"
assert result["finish_reason"] == "stop"
def test_with_reasoning(self, agent):
msg = _mock_assistant_msg(content="answer", reasoning="thinking")
result = agent._build_assistant_message(msg, "stop")
assert result["reasoning"] == "thinking"
def test_reasoning_content_preserved_separately(self, agent):
msg = _mock_assistant_msg(
content="answer",
reasoning="summary",
reasoning_content="provider scratchpad",
)
result = agent._build_assistant_message(msg, "stop")
assert result["reasoning_content"] == "provider scratchpad"
def test_with_tool_calls(self, agent):
tc = _mock_tool_call(name="web_search", arguments='{"q":"test"}', call_id="c1")
msg = _mock_assistant_msg(content="", tool_calls=[tc])
result = agent._build_assistant_message(msg, "tool_calls")
assert len(result["tool_calls"]) == 1
assert result["tool_calls"][0]["function"]["name"] == "web_search"
def test_with_reasoning_details(self, agent):
details = [{"type": "reasoning.summary", "text": "step1", "signature": "sig1"}]
msg = _mock_assistant_msg(content="ans", reasoning_details=details)
result = agent._build_assistant_message(msg, "stop")
assert "reasoning_details" in result
assert result["reasoning_details"][0]["text"] == "step1"
def test_empty_content(self, agent):
msg = _mock_assistant_msg(content=None)
result = agent._build_assistant_message(msg, "stop")
assert result["content"] == ""
def test_streaming_only_reasoning_promoted_to_reasoning_content(self, agent):
"""Refs #16844 / #16884. Streaming-only providers (glm, MiniMax,
gpt-5.x via aigw, Anthropic via openai-compat shims) accumulate
reasoning through delta chunks but never expose
``reasoning_content`` as a top-level attribute on the finalized
message — only ``reasoning`` (or the internal accumulator).
Without write-side promotion, the persisted message stores the
chain-of-thought under the internal ``reasoning`` key and omits
``reasoning_content``. When the user later replays that history
through a DeepSeek-v4 / Kimi thinking model, the missing field
causes HTTP 400 ("The reasoning_content in the thinking mode
must be passed back to the API.").
Fix: when ``reasoning_content`` wasn't written by an earlier
branch AND we captured reasoning text from streaming deltas,
promote it to ``reasoning_content`` at write time.
"""
# SDK-style object that exposes ``reasoning`` but NOT
# ``reasoning_content`` — the streaming-only provider shape.
msg = _mock_assistant_msg(content="answer", reasoning="hidden thinking")
assert not hasattr(msg, "reasoning_content")
result = agent._build_assistant_message(msg, "stop")
assert result["reasoning"] == "hidden thinking"
assert result["reasoning_content"] == "hidden thinking"
def test_sdk_reasoning_content_still_wins_over_fallback(self, agent):
"""Additive fallback must not override SDK-supplied reasoning_content.
When both ``reasoning`` and ``reasoning_content`` are present, the
SDK's own ``reasoning_content`` is authoritative (may carry
structured data the accumulator doesn't have).
"""
msg = _mock_assistant_msg(
content="answer",
reasoning="summary only",
reasoning_content="structured provider scratchpad",
)
result = agent._build_assistant_message(msg, "stop")
assert result["reasoning_content"] == "structured provider scratchpad"
def test_no_reasoning_text_leaves_field_absent(self, agent):
"""Non-thinking turns with no reasoning leave reasoning_content absent.
This preserves ``_copy_reasoning_content_for_api``'s downstream
tiers at replay time — cross-provider leak guard (#15748),
promote-from-``reasoning``, and DeepSeek/Kimi " "-pad — which
would all be bypassed if we eagerly wrote ``reasoning_content=" "``
on every assistant turn regardless of provider.
"""
msg = _mock_assistant_msg(content="plain answer")
result = agent._build_assistant_message(msg, "stop")
assert "reasoning_content" not in result
def test_tool_call_extra_content_preserved(self, agent):
"""Gemini thinking models attach extra_content with thought_signature
to tool calls. This must be preserved so subsequent API calls include it."""
tc = _mock_tool_call(
name="get_weather", arguments='{"city":"NYC"}', call_id="c2"
)
tc.extra_content = {"google": {"thought_signature": "abc123"}}
msg = _mock_assistant_msg(content="", tool_calls=[tc])
result = agent._build_assistant_message(msg, "tool_calls")
assert result["tool_calls"][0]["extra_content"] == {
"google": {"thought_signature": "abc123"}
}
def test_tool_call_without_extra_content(self, agent):
"""Standard tool calls (no thinking model) should not have extra_content."""
tc = _mock_tool_call(name="web_search", arguments="{}", call_id="c3")
msg = _mock_assistant_msg(content="", tool_calls=[tc])
result = agent._build_assistant_message(msg, "tool_calls")
assert "extra_content" not in result["tool_calls"][0]
def test_think_blocks_stripped_from_content(self, agent):
"""Inline blocks are stripped from stored content (#8878, #9568).
The reasoning is captured into ``msg['reasoning']`` via the inline
fallback in ``_extract_reasoning``; the raw tags in ``content`` are
redundant and leak to messaging platforms / pollute titles /
inflate context if left in place.
"""
msg = _mock_assistant_msg(
content="internal reasoningThe actual answer."
)
result = agent._build_assistant_message(msg, "stop")
assert "" not in result["content"]
assert "internal reasoning" not in result["content"]
assert "The actual answer." in result["content"]
# Reasoning preserved separately via inline extraction fallback
assert result["reasoning"] == "internal reasoning"
def test_think_blocks_stripped_preserves_normal_content(self, agent):
"""Content without reasoning tags passes through unchanged."""
msg = _mock_assistant_msg(content="No thinking here.")
result = agent._build_assistant_message(msg, "stop")
assert result["content"] == "No thinking here."
def test_memory_context_in_stored_content_is_preserved(self, agent):
"""`_build_assistant_message` must not silently mutate model output
containing literal markers — that's legitimate text
(e.g. documentation, code) that the model may emit. Streaming-path
leak prevention is handled by StreamingContextScrubber upstream."""
original = (
"\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"
)
msg = _mock_assistant_msg(content=original)
result = agent._build_assistant_message(msg, "stop")
assert "" in result["content"]
assert "Visible answer" in result["content"]
def test_unterminated_think_block_stripped(self, agent):
"""Unterminated block (MiniMax / NIM dropped close tag) is
fully stripped from stored content."""
msg = _mock_assistant_msg(
content="reasoning that never closes on this NIM endpoint"
)
result = agent._build_assistant_message(msg, "stop")
assert "" not in result["content"]
assert "reasoning that never closes" not in result["content"]
assert result["content"] == ""
class TestFormatToolsForSystemMessage:
def test_no_tools_returns_empty_array(self, agent):
agent.tools = []
assert agent._format_tools_for_system_message() == "[]"
def test_formats_single_tool(self, agent):
agent.tools = _make_tool_defs("web_search")
result = agent._format_tools_for_system_message()
parsed = json.loads(result)
assert len(parsed) == 1
assert parsed[0]["name"] == "web_search"
def test_formats_multiple_tools(self, agent):
agent.tools = _make_tool_defs("web_search", "terminal", "read_file")
result = agent._format_tools_for_system_message()
parsed = json.loads(result)
assert len(parsed) == 3
names = {t["name"] for t in parsed}
assert names == {"web_search", "terminal", "read_file"}
# ===================================================================
# Group 3: Conversation Loop Pieces (OpenAI mock)
# ===================================================================
class TestExecuteToolCalls:
def test_single_tool_executed(self, agent):
tc = _mock_tool_call(name="web_search", arguments='{"q":"test"}', call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
with patch(
"run_agent.handle_function_call", return_value="search result"
) as mock_hfc:
agent._execute_tool_calls(mock_msg, messages, "task-1")
# enabled_tools passes the agent's own valid_tool_names
args, kwargs = mock_hfc.call_args
assert args[:3] == ("web_search", {"q": "test"}, "task-1")
assert set(kwargs.get("enabled_tools", [])) == agent.valid_tool_names
assert len(messages) == 1
assert messages[0]["role"] == "tool"
assert "search result" in messages[0]["content"]
def test_interrupt_skips_remaining(self, agent):
tc1 = _mock_tool_call(name="web_search", arguments="{}", call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments="{}", call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch("run_agent._set_interrupt"):
agent.interrupt()
agent._execute_tool_calls(mock_msg, messages, "task-1")
# Both calls should be skipped with cancellation messages
assert len(messages) == 2
assert (
"cancelled" in messages[0]["content"].lower()
or "interrupted" in messages[0]["content"].lower()
)
def test_invalid_json_args_defaults_empty(self, agent):
tc = _mock_tool_call(
name="web_search", arguments="not valid json", call_id="c1"
)
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
with patch("run_agent.handle_function_call", return_value="ok") as mock_hfc:
agent._execute_tool_calls(mock_msg, messages, "task-1")
# Invalid JSON args should fall back to empty dict
args, kwargs = mock_hfc.call_args
assert args[:3] == ("web_search", {}, "task-1")
assert set(kwargs.get("enabled_tools", [])) == agent.valid_tool_names
assert len(messages) == 1
assert messages[0]["role"] == "tool"
assert messages[0]["tool_call_id"] == "c1"
def test_result_truncation_over_100k(self, agent, tmp_path, monkeypatch):
monkeypatch.setenv("HERMES_HOME", str(tmp_path / ".hermes"))
(tmp_path / ".hermes").mkdir()
tc = _mock_tool_call(name="web_search", arguments="{}", call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
big_result = "x" * 150_000
with patch("run_agent.handle_function_call", return_value=big_result):
agent._execute_tool_calls(mock_msg, messages, "task-1")
# Content should be replaced with persisted-output or truncation
assert len(messages[0]["content"]) < 150_000
assert ("Truncated" in messages[0]["content"] or "" in messages[0]["content"])
def test_quiet_tool_output_suppressed_when_progress_callback_present(self, agent):
tc = _mock_tool_call(name="web_search", arguments='{"q":"test"}', call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
agent.tool_progress_callback = lambda *args, **kwargs: None
with patch("run_agent.handle_function_call", return_value="search result"), \
patch.object(agent, "_safe_print") as mock_print:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_print.assert_not_called()
assert len(messages) == 1
assert messages[0]["role"] == "tool"
def test_quiet_tool_output_prints_without_progress_callback(self, agent):
tc = _mock_tool_call(name="web_search", arguments='{"q":"test"}', call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
agent.platform = "cli"
agent.tool_progress_callback = None
with patch("run_agent.handle_function_call", return_value="search result"), \
patch.object(agent, "_safe_print") as mock_print:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_print.assert_called_once()
assert "search" in str(mock_print.call_args.args[0]).lower()
assert len(messages) == 1
assert messages[0]["role"] == "tool"
def test_quiet_tool_output_suppressed_without_progress_callback_for_non_cli_agent(self, agent):
tc = _mock_tool_call(name="web_search", arguments='{"q":"test"}', call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
agent.platform = None
agent.tool_progress_callback = None
with patch("run_agent.handle_function_call", return_value="search result"), \
patch.object(agent, "_safe_print") as mock_print:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_print.assert_not_called()
assert len(messages) == 1
assert messages[0]["role"] == "tool"
def test_vprint_suppressed_in_parseable_quiet_mode(self, agent):
agent.suppress_status_output = True
with patch.object(agent, "_safe_print") as mock_print:
agent._vprint("status line", force=True)
agent._vprint("normal line")
mock_print.assert_not_called()
def test_run_conversation_suppresses_retry_noise_in_parseable_quiet_mode(self, agent):
class _RateLimitError(Exception):
status_code = 429
def __str__(self):
return "Error code: 429 - Rate limit exceeded."
responses = [_RateLimitError(), _mock_response(content="Recovered")]
def _fake_api_call(api_kwargs):
result = responses.pop(0)
if isinstance(result, Exception):
raise result
return result
agent.suppress_status_output = True
agent._interruptible_api_call = _fake_api_call
agent._persist_session = lambda *args, **kwargs: None
agent._save_trajectory = lambda *args, **kwargs: None
agent._save_session_log = lambda *args, **kwargs: None
captured = io.StringIO()
agent._print_fn = lambda *args, **kw: print(*args, file=captured, **kw)
with patch("run_agent.time.sleep", return_value=None):
result = agent.run_conversation("hello")
assert result["completed"] is True
assert result["final_response"] == "Recovered"
output = captured.getvalue()
assert "API call failed" not in output
assert "Rate limit reached" not in output
class TestConcurrentToolExecution:
"""Tests for _execute_tool_calls_concurrent and dispatch logic."""
def test_single_tool_uses_sequential_path(self, agent):
"""Single tool call should use sequential path, not concurrent."""
tc = _mock_tool_call(name="web_search", arguments='{"q":"test"}', call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_clarify_forces_sequential(self, agent):
"""Batch containing clarify should use sequential path."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="clarify", arguments='{"question":"ok?"}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_multiple_tools_uses_concurrent_path(self, agent):
"""Multiple read-only tools should use concurrent path."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="read_file", arguments='{"path":"x.py"}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_con.assert_called_once()
mock_seq.assert_not_called()
def test_terminal_batch_forces_sequential(self, agent):
"""Stateful tools should not share the concurrent execution path."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="terminal", arguments='{"command":"pwd"}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_write_batch_forces_sequential(self, agent):
"""File mutations should stay ordered within a turn."""
tc1 = _mock_tool_call(name="read_file", arguments='{"path":"x.py"}', call_id="c1")
tc2 = _mock_tool_call(name="write_file", arguments='{"path":"x.py","content":"print(1)"}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_disjoint_write_batch_uses_concurrent_path(self, agent):
"""Independent file writes should still run concurrently."""
tc1 = _mock_tool_call(
name="write_file",
arguments='{"path":"src/a.py","content":"print(1)"}',
call_id="c1",
)
tc2 = _mock_tool_call(
name="write_file",
arguments='{"path":"src/b.py","content":"print(2)"}',
call_id="c2",
)
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_con.assert_called_once()
mock_seq.assert_not_called()
def test_overlapping_write_batch_forces_sequential(self, agent):
"""Writes to the same file must stay ordered."""
tc1 = _mock_tool_call(
name="write_file",
arguments='{"path":"src/a.py","content":"print(1)"}',
call_id="c1",
)
tc2 = _mock_tool_call(
name="patch",
arguments='{"path":"src/a.py","old_string":"1","new_string":"2"}',
call_id="c2",
)
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_malformed_json_args_forces_sequential(self, agent):
"""Unparseable tool arguments should fall back to sequential."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments="NOT JSON {{{", call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_non_dict_args_forces_sequential(self, agent):
"""Tool arguments that parse to a non-dict type should fall back to sequential."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments='"just a string"', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch.object(agent, "_execute_tool_calls_sequential") as mock_seq:
with patch.object(agent, "_execute_tool_calls_concurrent") as mock_con:
agent._execute_tool_calls(mock_msg, messages, "task-1")
mock_seq.assert_called_once()
mock_con.assert_not_called()
def test_concurrent_executes_all_tools(self, agent):
"""Concurrent path should execute all tools and append results in order."""
tc1 = _mock_tool_call(name="web_search", arguments='{"q":"alpha"}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments='{"q":"beta"}', call_id="c2")
tc3 = _mock_tool_call(name="web_search", arguments='{"q":"gamma"}', call_id="c3")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2, tc3])
messages = []
call_log = []
def fake_handle(name, args, task_id, **kwargs):
call_log.append(name)
return json.dumps({"result": args.get("q", "")})
with patch("run_agent.handle_function_call", side_effect=fake_handle):
agent._execute_tool_calls_concurrent(mock_msg, messages, "task-1")
assert len(messages) == 3
# Results must be in original order
assert messages[0]["tool_call_id"] == "c1"
assert messages[1]["tool_call_id"] == "c2"
assert messages[2]["tool_call_id"] == "c3"
# All should be tool messages
assert all(m["role"] == "tool" for m in messages)
# Content should contain the query results
assert "alpha" in messages[0]["content"]
assert "beta" in messages[1]["content"]
assert "gamma" in messages[2]["content"]
def test_concurrent_preserves_order_despite_timing(self, agent):
"""Even if tools finish in different order, messages should be in original order."""
import time as _time
tc1 = _mock_tool_call(name="web_search", arguments='{"q":"slow"}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments='{"q":"fast"}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
def fake_handle(name, args, task_id, **kwargs):
q = args.get("q", "")
if q == "slow":
_time.sleep(0.1) # Slow tool
return f"result_{q}"
with patch("run_agent.handle_function_call", side_effect=fake_handle):
agent._execute_tool_calls_concurrent(mock_msg, messages, "task-1")
assert messages[0]["tool_call_id"] == "c1"
assert "result_slow" in messages[0]["content"]
assert messages[1]["tool_call_id"] == "c2"
assert "result_fast" in messages[1]["content"]
def test_concurrent_handles_tool_error(self, agent):
"""If one tool raises, others should still complete."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments='{}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
call_count = [0]
def fake_handle(name, args, task_id, **kwargs):
call_count[0] += 1
if call_count[0] == 1:
raise RuntimeError("boom")
return "success"
with patch("run_agent.handle_function_call", side_effect=fake_handle):
agent._execute_tool_calls_concurrent(mock_msg, messages, "task-1")
assert len(messages) == 2
# First tool should have error
assert "Error" in messages[0]["content"] or "boom" in messages[0]["content"]
# Second tool should succeed
assert "success" in messages[1]["content"]
def test_concurrent_interrupt_before_start(self, agent):
"""If interrupt is requested before concurrent execution, all tools are skipped."""
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="read_file", arguments='{}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
with patch("run_agent._set_interrupt"):
agent.interrupt()
agent._execute_tool_calls_concurrent(mock_msg, messages, "task-1")
assert len(messages) == 2
assert "cancelled" in messages[0]["content"].lower() or "skipped" in messages[0]["content"].lower()
assert "cancelled" in messages[1]["content"].lower() or "skipped" in messages[1]["content"].lower()
def test_concurrent_truncates_large_results(self, agent, tmp_path, monkeypatch):
"""Concurrent path should save oversized results to file."""
monkeypatch.setenv("HERMES_HOME", str(tmp_path / ".hermes"))
(tmp_path / ".hermes").mkdir()
tc1 = _mock_tool_call(name="web_search", arguments='{}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments='{}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
big_result = "x" * 150_000
with patch("run_agent.handle_function_call", return_value=big_result):
agent._execute_tool_calls_concurrent(mock_msg, messages, "task-1")
assert len(messages) == 2
for m in messages:
assert len(m["content"]) < 150_000
assert ("Truncated" in m["content"] or "" in m["content"])
def test_invoke_tool_dispatches_to_handle_function_call(self, agent):
"""_invoke_tool should route regular tools through handle_function_call."""
with patch("run_agent.handle_function_call", return_value="result") as mock_hfc:
result = agent._invoke_tool("web_search", {"q": "test"}, "task-1")
mock_hfc.assert_called_once_with(
"web_search", {"q": "test"}, "task-1",
tool_call_id=None,
session_id=agent.session_id,
enabled_tools=list(agent.valid_tool_names),
skip_pre_tool_call_hook=True,
)
assert result == "result"
def test_sequential_tool_callbacks_fire_in_order(self, agent):
tool_call = _mock_tool_call(name="web_search", arguments='{"query":"hello"}', call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tool_call])
messages = []
starts = []
completes = []
agent.tool_start_callback = lambda tool_call_id, function_name, function_args: starts.append((tool_call_id, function_name, function_args))
agent.tool_complete_callback = lambda tool_call_id, function_name, function_args, function_result: completes.append((tool_call_id, function_name, function_args, function_result))
with patch("run_agent.handle_function_call", return_value='{"success": true}'):
agent._execute_tool_calls_sequential(mock_msg, messages, "task-1")
assert starts == [("c1", "web_search", {"query": "hello"})]
assert completes == [("c1", "web_search", {"query": "hello"}, '{"success": true}')]
def test_concurrent_tool_callbacks_fire_for_each_tool(self, agent):
tc1 = _mock_tool_call(name="web_search", arguments='{"query":"one"}', call_id="c1")
tc2 = _mock_tool_call(name="web_search", arguments='{"query":"two"}', call_id="c2")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tc1, tc2])
messages = []
starts = []
completes = []
agent.tool_start_callback = lambda tool_call_id, function_name, function_args: starts.append((tool_call_id, function_name, function_args))
agent.tool_complete_callback = lambda tool_call_id, function_name, function_args, function_result: completes.append((tool_call_id, function_name, function_args, function_result))
with patch("run_agent.handle_function_call", side_effect=['{"id":1}', '{"id":2}']):
agent._execute_tool_calls_concurrent(mock_msg, messages, "task-1")
assert starts == [
("c1", "web_search", {"query": "one"}),
("c2", "web_search", {"query": "two"}),
]
assert len(completes) == 2
assert {entry[0] for entry in completes} == {"c1", "c2"}
assert {entry[3] for entry in completes} == {'{"id":1}', '{"id":2}'}
def test_invoke_tool_handles_agent_level_tools(self, agent):
"""_invoke_tool should handle todo tool directly."""
with patch("tools.todo_tool.todo_tool", return_value='{"ok":true}') as mock_todo:
result = agent._invoke_tool("todo", {"todos": []}, "task-1")
mock_todo.assert_called_once()
assert "ok" in result
def test_invoke_tool_blocked_returns_error_and_skips_execution(self, agent, monkeypatch):
"""_invoke_tool should return error JSON when a plugin blocks the tool."""
monkeypatch.setattr(
"hermes_cli.plugins.get_pre_tool_call_block_message",
lambda *args, **kwargs: "Blocked by test policy",
)
with patch("tools.todo_tool.todo_tool", side_effect=AssertionError("should not run")) as mock_todo:
result = agent._invoke_tool("todo", {"todos": []}, "task-1")
assert json.loads(result) == {"error": "Blocked by test policy"}
mock_todo.assert_not_called()
def test_invoke_tool_blocked_skips_handle_function_call(self, agent, monkeypatch):
"""Blocked registry tools should not reach handle_function_call."""
monkeypatch.setattr(
"hermes_cli.plugins.get_pre_tool_call_block_message",
lambda *args, **kwargs: "Blocked",
)
with patch("run_agent.handle_function_call", side_effect=AssertionError("should not run")):
result = agent._invoke_tool("web_search", {"q": "test"}, "task-1")
assert json.loads(result) == {"error": "Blocked"}
def test_sequential_blocked_tool_skips_checkpoints_and_callbacks(self, agent, monkeypatch):
"""Sequential path: blocked tool should not trigger checkpoints or start callbacks."""
tool_call = _mock_tool_call(name="write_file",
arguments='{"path":"test.txt","content":"hello"}',
call_id="c1")
mock_msg = _mock_assistant_msg(content="", tool_calls=[tool_call])
messages = []
monkeypatch.setattr(
"hermes_cli.plugins.get_pre_tool_call_block_message",
lambda *args, **kwargs: "Blocked by policy",
)
agent._checkpoint_mgr.enabled = True
agent._checkpoint_mgr.ensure_checkpoint = MagicMock(
side_effect=AssertionError("checkpoint should not run")
)
starts = []
agent.tool_start_callback = lambda *a: starts.append(a)
with patch("run_agent.handle_function_call", side_effect=AssertionError("should not run")):
agent._execute_tool_calls_sequential(mock_msg, messages, "task-1")
agent._checkpoint_mgr.ensure_checkpoint.assert_not_called()
assert starts == []
assert len(messages) == 1
assert messages[0]["role"] == "tool"
assert json.loads(messages[0]["content"]) == {"error": "Blocked by policy"}
def test_blocked_memory_tool_does_not_reset_counter(self, agent, monkeypatch):
"""Blocked memory tool should not reset the nudge counter."""
agent._turns_since_memory = 5
monkeypatch.setattr(
"hermes_cli.plugins.get_pre_tool_call_block_message",
lambda *args, **kwargs: "Blocked",
)
with patch("tools.memory_tool.memory_tool", side_effect=AssertionError("should not run")):
result = agent._invoke_tool(
"memory", {"action": "add", "target": "memory", "content": "x"}, "task-1",
)
assert json.loads(result) == {"error": "Blocked"}
assert agent._turns_since_memory == 5
class TestPathsOverlap:
"""Unit tests for the _paths_overlap helper."""
def test_same_path_overlaps(self):
from run_agent import _paths_overlap
assert _paths_overlap(Path("src/a.py"), Path("src/a.py"))
def test_siblings_do_not_overlap(self):
from run_agent import _paths_overlap
assert not _paths_overlap(Path("src/a.py"), Path("src/b.py"))
def test_parent_child_overlap(self):
from run_agent import _paths_overlap
assert _paths_overlap(Path("src"), Path("src/sub/a.py"))
def test_different_roots_do_not_overlap(self):
from run_agent import _paths_overlap
assert not _paths_overlap(Path("src/a.py"), Path("other/a.py"))
def test_nested_vs_flat_do_not_overlap(self):
from run_agent import _paths_overlap
assert not _paths_overlap(Path("src/sub/a.py"), Path("src/a.py"))
def test_empty_paths_do_not_overlap(self):
from run_agent import _paths_overlap
assert not _paths_overlap(Path(""), Path(""))
def test_one_empty_path_does_not_overlap(self):
from run_agent import _paths_overlap
assert not _paths_overlap(Path(""), Path("src/a.py"))
assert not _paths_overlap(Path("src/a.py"), Path(""))
class TestParallelScopePathNormalization:
def test_extract_parallel_scope_path_normalizes_relative_to_cwd(self, tmp_path, monkeypatch):
from run_agent import _extract_parallel_scope_path
monkeypatch.chdir(tmp_path)
scoped = _extract_parallel_scope_path("write_file", {"path": "./notes.txt"})
assert scoped == tmp_path / "notes.txt"
def test_extract_parallel_scope_path_treats_relative_and_absolute_same_file_as_same_scope(self, tmp_path, monkeypatch):
from run_agent import _extract_parallel_scope_path, _paths_overlap
monkeypatch.chdir(tmp_path)
abs_path = tmp_path / "notes.txt"
rel_scoped = _extract_parallel_scope_path("write_file", {"path": "notes.txt"})
abs_scoped = _extract_parallel_scope_path("write_file", {"path": str(abs_path)})
assert rel_scoped == abs_scoped
assert _paths_overlap(rel_scoped, abs_scoped)
def test_should_parallelize_tool_batch_rejects_same_file_with_mixed_path_spellings(self, tmp_path, monkeypatch):
from run_agent import _should_parallelize_tool_batch
monkeypatch.chdir(tmp_path)
tc1 = _mock_tool_call(name="write_file", arguments='{"path":"notes.txt","content":"one"}', call_id="c1")
tc2 = _mock_tool_call(name="write_file", arguments=f'{{"path":"{tmp_path / "notes.txt"}","content":"two"}}', call_id="c2")
assert not _should_parallelize_tool_batch([tc1, tc2])
class TestHandleMaxIterations:
def test_returns_summary(self, agent):
resp = _mock_response(content="Here is a summary of what I did.")
agent.client.chat.completions.create.return_value = resp
agent._cached_system_prompt = "You are helpful."
messages = [{"role": "user", "content": "do stuff"}]
result = agent._handle_max_iterations(messages, 60)
assert isinstance(result, str)
assert len(result) > 0
assert "summary" in result.lower()
def test_api_failure_returns_error(self, agent):
agent.client.chat.completions.create.side_effect = Exception("API down")
agent._cached_system_prompt = "You are helpful."
messages = [{"role": "user", "content": "do stuff"}]
result = agent._handle_max_iterations(messages, 60)
assert isinstance(result, str)
assert "error" in result.lower()
assert "API down" in result
def test_summary_skips_reasoning_for_unsupported_openrouter_model(self, agent):
agent.base_url = "https://openrouter.ai/api/v1"
agent.model = "minimax/minimax-m2.5"
resp = _mock_response(content="Summary")
agent.client.chat.completions.create.return_value = resp
agent._cached_system_prompt = "You are helpful."
messages = [{"role": "user", "content": "do stuff"}]
result = agent._handle_max_iterations(messages, 60)
assert result == "Summary"
kwargs = agent.client.chat.completions.create.call_args.kwargs
assert "reasoning" not in kwargs.get("extra_body", {})
def test_codex_summary_sanitizes_orphan_tool_results(self, agent):
agent.api_mode = "codex_responses"
agent.provider = "openai-codex"
agent.base_url = "https://chatgpt.com/backend-api/codex"
agent._base_url_lower = agent.base_url.lower()
agent._base_url_hostname = "chatgpt.com"
agent.model = "gpt-5.5"
agent._cached_system_prompt = "You are helpful."
captured = {}
def fake_run_codex_stream(kwargs):
captured.update(kwargs)
return SimpleNamespace(
status="completed",
output=[
SimpleNamespace(
type="message",
status="completed",
content=[SimpleNamespace(type="output_text", text="Summary")],
)
],
)
messages = [
{"role": "user", "content": "do stuff"},
{
"role": "tool",
"tool_call_id": "call_orphan",
"content": "orphaned result from compressed history",
},
]
with patch.object(agent, "_run_codex_stream", side_effect=fake_run_codex_stream):
result = agent._handle_max_iterations(messages, 90)
assert result == "Summary"
input_items = captured["input"]
assert not any(
item.get("type") == "function_call_output"
and item.get("call_id") == "call_orphan"
for item in input_items
)
def test_api_sanitizer_matches_responses_call_id_when_id_differs(self, agent):
messages = [
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "fc_123",
"call_id": "call_123",
"response_item_id": "fc_123",
"type": "function",
"function": {"name": "web_search", "arguments": "{}"},
}
],
},
{"role": "tool", "tool_call_id": "call_123", "content": "result"},
]
sanitized = agent._sanitize_api_messages(messages)
assert [m.get("tool_call_id") for m in sanitized if m.get("role") == "tool"] == [
"call_123"
]
class TestRunConversation:
"""Tests for the main run_conversation method.
Each test mocks client.chat.completions.create to return controlled
responses, exercising different code paths without real API calls.
"""
def _setup_agent(self, agent):
"""Common setup for run_conversation tests."""
agent._cached_system_prompt = "You are helpful."
agent._use_prompt_caching = False
agent.tool_delay = 0
agent.compression_enabled = False
agent.save_trajectories = False
def test_stop_finish_reason_returns_response(self, agent):
self._setup_agent(agent)
resp = _mock_response(content="Final answer", finish_reason="stop")
agent.client.chat.completions.create.return_value = resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
assert result["final_response"] == "Final answer"
assert result["completed"] is True
def test_tool_calls_then_stop(self, agent):
self._setup_agent(agent)
tc = _mock_tool_call(name="web_search", arguments="{}", call_id="c1")
resp1 = _mock_response(content="", finish_reason="tool_calls", tool_calls=[tc])
resp2 = _mock_response(content="Done searching", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [resp1, resp2]
with (
patch("run_agent.handle_function_call", return_value="search result") as mock_handle_function_call,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("search something")
assert result["final_response"] == "Done searching"
assert result["api_calls"] == 2
assert mock_handle_function_call.call_args.kwargs["tool_call_id"] == "c1"
assert mock_handle_function_call.call_args.kwargs["session_id"] == agent.session_id
def test_request_scoped_api_hooks_fire_for_each_api_call(self, agent):
self._setup_agent(agent)
tc = _mock_tool_call(name="web_search", arguments="{}", call_id="c1")
resp1 = _mock_response(content="", finish_reason="tool_calls", tool_calls=[tc])
resp2 = _mock_response(content="Done searching", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [resp1, resp2]
hook_calls = []
def _record_hook(name, **kwargs):
hook_calls.append((name, kwargs))
return []
with (
patch("run_agent.handle_function_call", return_value="search result"),
patch("hermes_cli.plugins.invoke_hook", side_effect=_record_hook),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("search something")
assert result["final_response"] == "Done searching"
pre_request_calls = [kw for name, kw in hook_calls if name == "pre_api_request"]
post_request_calls = [kw for name, kw in hook_calls if name == "post_api_request"]
assert len(pre_request_calls) == 2
assert len(post_request_calls) == 2
assert [call["api_call_count"] for call in pre_request_calls] == [1, 2]
assert [call["api_call_count"] for call in post_request_calls] == [1, 2]
assert all(call["session_id"] == agent.session_id for call in pre_request_calls)
assert all("message_count" in c and "messages" not in c for c in pre_request_calls)
assert all("usage" in c and "response" not in c for c in post_request_calls)
def test_content_with_tool_calls_stays_silent_for_non_cli_quiet_mode(self, agent):
self._setup_agent(agent)
agent.platform = None
tc = _mock_tool_call(name="web_search", arguments="{}", call_id="c1")
resp1 = _mock_response(
content="I'll search for that.",
finish_reason="tool_calls",
tool_calls=[tc],
)
resp2 = _mock_response(content="Done searching", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [resp1, resp2]
with (
patch("run_agent.handle_function_call", return_value="search result"),
patch.object(agent, "_safe_print") as mock_print,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("search something")
assert result["final_response"] == "Done searching"
mock_print.assert_not_called()
def test_interrupt_breaks_loop(self, agent):
self._setup_agent(agent)
def interrupt_side_effect(api_kwargs):
agent._interrupt_requested = True
raise InterruptedError("Agent interrupted during API call")
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch("run_agent._set_interrupt"),
patch.object(
agent, "_interruptible_api_call", side_effect=interrupt_side_effect
),
):
result = agent.run_conversation("hello")
assert result["interrupted"] is True
def test_invalid_tool_name_retry(self, agent):
"""Model hallucinates an invalid tool name, agent retries and succeeds."""
self._setup_agent(agent)
bad_tc = _mock_tool_call(name="nonexistent_tool", arguments="{}", call_id="c1")
resp_bad = _mock_response(
content="", finish_reason="tool_calls", tool_calls=[bad_tc]
)
resp_good = _mock_response(content="Got it", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [resp_bad, resp_good]
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("do something")
assert result["final_response"] == "Got it"
assert result["completed"] is True
assert result["api_calls"] == 2
def test_reasoning_only_local_resumed_no_compression_triggered(self, agent):
"""Reasoning-only responses no longer trigger compression — prefill then accepted."""
self._setup_agent(agent)
agent.base_url = "http://127.0.0.1:1234/v1"
agent.compression_enabled = True
empty_resp = _mock_response(
content=None,
finish_reason="stop",
reasoning_content="reasoning only",
)
prefill = [
{"role": "user", "content": "old question"},
{"role": "assistant", "content": "old answer"},
]
# 6 responses: original + 2 prefill + 3 retries after prefill exhaustion
with (
patch.object(agent, "_interruptible_api_call", side_effect=[empty_resp] * 6),
patch.object(agent, "_compress_context") as mock_compress,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello", conversation_history=prefill)
mock_compress.assert_not_called() # no compression triggered
assert result["completed"] is True
assert result["final_response"] == "(empty)"
assert result["api_calls"] == 6 # 1 original + 2 prefill + 3 retries
def test_reasoning_only_response_prefill_then_empty(self, agent):
"""Structured reasoning-only triggers prefill (2), then retries (3), then (empty)."""
self._setup_agent(agent)
empty_resp = _mock_response(
content=None,
finish_reason="stop",
reasoning_content="structured reasoning answer",
)
# 6 responses: 1 original + 2 prefill + 3 retries after prefill exhaustion
agent.client.chat.completions.create.side_effect = [empty_resp] * 6
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("answer me")
assert result["completed"] is True
assert result["final_response"] == "(empty)"
assert result["api_calls"] == 6 # 1 original + 2 prefill + 3 retries
def test_reasoning_only_prefill_succeeds_on_continuation(self, agent):
"""When prefill continuation produces content, it becomes the final response."""
self._setup_agent(agent)
empty_resp = _mock_response(
content=None,
finish_reason="stop",
reasoning_content="structured reasoning answer",
)
content_resp = _mock_response(
content="Here is the actual answer.",
finish_reason="stop",
)
agent.client.chat.completions.create.side_effect = [empty_resp, content_resp]
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("answer me")
assert result["completed"] is True
assert result["final_response"] == "Here is the actual answer."
assert result["api_calls"] == 2 # 1 original + 1 prefill continuation
# Prefill message should be cleaned up — no consecutive assistant messages
roles = [m.get("role") for m in result["messages"]]
for i in range(len(roles) - 1):
if roles[i] == "assistant" and roles[i + 1] == "assistant":
raise AssertionError("Consecutive assistant messages found in history")
def test_truly_empty_response_retries_3_times_then_empty(self, agent):
"""Truly empty response (no content, no reasoning) retries 3 times then falls through to (empty)."""
self._setup_agent(agent)
agent.base_url = "http://127.0.0.1:1234/v1"
empty_resp = _mock_response(content=None, finish_reason="stop")
# 4 responses: 1 original + 3 nudge retries, all empty
agent.client.chat.completions.create.side_effect = [
empty_resp, empty_resp, empty_resp, empty_resp,
]
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("answer me")
assert result["completed"] is True
assert result["final_response"] == "(empty)"
assert result["api_calls"] == 4 # 1 original + 3 retries
def test_truly_empty_response_succeeds_on_nudge(self, agent):
"""Model produces content after being nudged for empty response."""
self._setup_agent(agent)
agent.base_url = "http://127.0.0.1:1234/v1"
empty_resp = _mock_response(content=None, finish_reason="stop")
content_resp = _mock_response(
content="Here is the actual answer.",
finish_reason="stop",
)
# 1 empty response, then model produces content on nudge
agent.client.chat.completions.create.side_effect = [empty_resp, content_resp]
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("answer me")
assert result["completed"] is True
assert result["final_response"] == "Here is the actual answer."
assert result["api_calls"] == 2 # 1 original + 1 nudge retry
def test_empty_response_triggers_fallback_provider(self, agent):
"""After 3 empty retries, fallback provider is activated and produces content."""
self._setup_agent(agent)
agent.base_url = "http://127.0.0.1:1234/v1"
# Configure a fallback chain
agent._fallback_chain = [{"provider": "openrouter", "model": "anthropic/claude-sonnet-4"}]
agent._fallback_index = 0
agent._fallback_activated = False
empty_resp = _mock_response(content=None, finish_reason="stop")
content_resp = _mock_response(content="Fallback answer.", finish_reason="stop")
# 4 empty (1 orig + 3 retries), then fallback model answers
agent.client.chat.completions.create.side_effect = [
empty_resp, empty_resp, empty_resp, empty_resp, content_resp,
]
fallback_called = {"called": False}
def _mock_fallback():
fallback_called["called"] = True
# Simulate what _try_activate_fallback does: just advance the
# index and set the flag (the client is already mocked).
agent._fallback_index = 1
agent._fallback_activated = True
agent.model = "anthropic/claude-sonnet-4"
agent.provider = "openrouter"
return True
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch.object(agent, "_try_activate_fallback", side_effect=_mock_fallback),
):
result = agent.run_conversation("answer me")
assert fallback_called["called"], "Fallback should have been triggered"
assert result["completed"] is True
assert result["final_response"] == "Fallback answer."
def test_empty_response_fallback_also_empty_returns_empty(self, agent):
"""If fallback also returns empty, final response is (empty)."""
self._setup_agent(agent)
agent.base_url = "http://127.0.0.1:1234/v1"
agent._fallback_chain = [{"provider": "openrouter", "model": "anthropic/claude-sonnet-4"}]
agent._fallback_index = 0
agent._fallback_activated = False
empty_resp = _mock_response(content=None, finish_reason="stop")
# 4 empty from primary (1 + 3 retries), fallback activated,
# then 4 more empty from fallback (1 + 3 retries), no more fallbacks
agent.client.chat.completions.create.side_effect = [
empty_resp, empty_resp, empty_resp, empty_resp, # primary exhausted
empty_resp, empty_resp, empty_resp, empty_resp, # fallback exhausted
]
def _mock_fallback():
if agent._fallback_index >= len(agent._fallback_chain):
return False
agent._fallback_index += 1
agent._fallback_activated = True
agent.model = "anthropic/claude-sonnet-4"
agent.provider = "openrouter"
return True
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch.object(agent, "_try_activate_fallback", side_effect=_mock_fallback),
):
result = agent.run_conversation("answer me")
assert result["completed"] is True
assert result["final_response"] == "(empty)"
def test_empty_response_emits_status_for_gateway(self, agent):
"""_emit_status is called during empty retries so gateway users see feedback."""
self._setup_agent(agent)
agent.base_url = "http://127.0.0.1:1234/v1"
empty_resp = _mock_response(content=None, finish_reason="stop")
# 4 empty: 1 original + 3 retries, all empty, no fallback
agent.client.chat.completions.create.side_effect = [
empty_resp, empty_resp, empty_resp, empty_resp,
]
status_messages = []
def _capture_status(msg):
status_messages.append(msg)
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch.object(agent, "_emit_status", side_effect=_capture_status),
):
result = agent.run_conversation("answer me")
assert result["final_response"] == "(empty)"
# Should have emitted retry statuses (3 retries) + final failure
retry_msgs = [m for m in status_messages if "retrying" in m.lower()]
assert len(retry_msgs) == 3, f"Expected 3 retry status messages, got {len(retry_msgs)}: {status_messages}"
failure_msgs = [m for m in status_messages if "no content" in m.lower() or "no fallback" in m.lower()]
assert len(failure_msgs) >= 1, f"Expected at least 1 failure status, got: {status_messages}"
def test_partial_stream_recovery_uses_streamed_content(self, agent):
"""When streaming fails after partial delivery, recovered partial content becomes final response."""
self._setup_agent(agent)
# Simulate a partial-stream-stub response: content recovered from streaming
partial_resp = _mock_response(
content="Here is the partial answer that was stream",
finish_reason="stop",
)
agent.client.chat.completions.create.return_value = partial_resp
# Simulate that streaming had already delivered this text
agent._current_streamed_assistant_text = "Here is the partial answer that was stream"
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("explain something")
# The partial content should be used as-is (not empty, not retried)
assert result["completed"] is True
assert result["final_response"] == "Here is the partial answer that was stream"
assert result["api_calls"] == 1 # No retries
def test_partial_stream_recovery_on_empty_stub(self, agent):
"""When stub response has no content but text was streamed, use streamed text."""
self._setup_agent(agent)
# Stub response with no content (old behavior before fix)
empty_stub = _mock_response(content=None, finish_reason="stop")
def _fake_api_call(api_kwargs):
# Simulate what streaming does: accumulate text before returning
# a stub with no content (connection died mid-stream)
agent._current_streamed_assistant_text = "The answer to your question is that"
return empty_stub
status_messages = []
def _capture_status(msg):
status_messages.append(msg)
with (
patch.object(agent, "_interruptible_api_call", side_effect=_fake_api_call),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch.object(agent, "_emit_status", side_effect=_capture_status),
):
result = agent.run_conversation("ask me")
# Should recover partial streamed content, not fall through to (empty)
assert result["completed"] is True
assert result["final_response"] == "The answer to your question is that"
assert result["api_calls"] == 1 # No wasted retries
# Should emit the stream-interrupted status, NOT the empty-retry status
recovery_msgs = [m for m in status_messages if "stream interrupted" in m.lower()]
assert len(recovery_msgs) >= 1, f"Expected stream recovery status, got: {status_messages}"
# Should NOT have retry statuses
retry_msgs = [m for m in status_messages if "retrying" in m.lower()]
assert len(retry_msgs) == 0, f"Should not retry when stream content exists: {status_messages}"
def test_partial_stream_recovery_preempts_prior_turn_fallback(self, agent):
"""Partial streamed content takes priority over _last_content_with_tools fallback."""
self._setup_agent(agent)
# Set up the prior-turn fallback content (from a previous turn with tool calls)
agent._last_content_with_tools = "Old content from prior turn with tools"
# Stub response with no content
empty_stub = _mock_response(content=None, finish_reason="stop")
def _fake_api_call(api_kwargs):
# Simulate partial streaming before connection death
agent._current_streamed_assistant_text = "Fresh partial content from this turn"
return empty_stub
with (
patch.object(agent, "_interruptible_api_call", side_effect=_fake_api_call),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("question")
# Should use the streamed content, not the old prior-turn fallback
assert result["final_response"] == "Fresh partial content from this turn"
assert result["api_calls"] == 1
def test_nous_401_refreshes_after_remint_and_retries(self, agent):
self._setup_agent(agent)
agent.provider = "nous"
agent.api_mode = "chat_completions"
calls = {"api": 0, "refresh": 0}
class _UnauthorizedError(RuntimeError):
def __init__(self):
super().__init__("Error code: 401 - unauthorized")
self.status_code = 401
def _fake_api_call(api_kwargs):
calls["api"] += 1
if calls["api"] == 1:
raise _UnauthorizedError()
return _mock_response(
content="Recovered after remint", finish_reason="stop"
)
def _fake_refresh(*, force=True):
calls["refresh"] += 1
assert force is True
return True
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch.object(agent, "_interruptible_api_call", side_effect=_fake_api_call),
patch.object(
agent, "_try_refresh_nous_client_credentials", side_effect=_fake_refresh
),
):
result = agent.run_conversation("hello")
assert calls["api"] == 2
assert calls["refresh"] == 1
assert result["completed"] is True
assert result["final_response"] == "Recovered after remint"
def test_context_compression_triggered(self, agent):
"""When compressor says should_compress, compression runs."""
self._setup_agent(agent)
agent.compression_enabled = True
tc = _mock_tool_call(name="web_search", arguments="{}", call_id="c1")
resp1 = _mock_response(content="", finish_reason="tool_calls", tool_calls=[tc])
resp2 = _mock_response(content="All done", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [resp1, resp2]
with (
patch("run_agent.handle_function_call", return_value="result"),
patch.object(
agent.context_compressor, "should_compress", return_value=True
),
patch.object(agent, "_compress_context") as mock_compress,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
# _compress_context should return (messages, system_prompt)
mock_compress.return_value = (
[{"role": "user", "content": "search something"}],
"compressed system prompt",
)
result = agent.run_conversation("search something")
mock_compress.assert_called_once()
assert result["final_response"] == "All done"
assert result["completed"] is True
def test_glm_prompt_exceeds_max_length_triggers_compression(self, agent):
"""GLM/Z.AI uses 'Prompt exceeds max length' for context overflow."""
self._setup_agent(agent)
err_400 = Exception(
"Error code: 400 - {'error': {'code': '1261', 'message': 'Prompt exceeds max length'}}"
)
err_400.status_code = 400
ok_resp = _mock_response(content="Recovered after compression", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [err_400, ok_resp]
prefill = [
{"role": "user", "content": "previous question"},
{"role": "assistant", "content": "previous answer"},
]
with (
patch.object(agent, "_compress_context") as mock_compress,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
mock_compress.return_value = (
[{"role": "user", "content": "hello"}],
"compressed system prompt",
)
result = agent.run_conversation("hello", conversation_history=prefill)
mock_compress.assert_called_once()
assert result["final_response"] == "Recovered after compression"
assert result["completed"] is True
def test_minimax_delta_overflow_keeps_known_context_length(self, agent):
"""MiniMax reports overflow deltas like 'limit (2013)' without the real window.
Keep the known 204,800-token window and compress instead of probing down
to the generic 128K fallback tier.
"""
self._setup_agent(agent)
agent.provider = "minimax"
agent.model = "MiniMax-M2.7-highspeed"
agent.base_url = "https://api.minimax.io/anthropic"
agent.context_compressor.context_length = 204_800
agent.context_compressor.threshold_tokens = int(
agent.context_compressor.context_length * agent.context_compressor.threshold_percent
)
err_400 = Exception(
"HTTP 400: invalid params, context window exceeds limit (2013)"
)
err_400.status_code = 400
ok_resp = _mock_response(content="Recovered after compression", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [err_400, ok_resp]
prefill = [
{"role": "user", "content": "previous question"},
{"role": "assistant", "content": "previous answer"},
]
with (
patch.object(agent, "_compress_context") as mock_compress,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
mock_compress.return_value = (
[{"role": "user", "content": "hello"}],
"compressed system prompt",
)
result = agent.run_conversation("hello", conversation_history=prefill)
mock_compress.assert_called_once()
assert agent.context_compressor.context_length == 204_800
assert agent.context_compressor._context_probed is False
assert result["final_response"] == "Recovered after compression"
assert result["completed"] is True
def test_non_minimax_delta_overflow_still_probes_down(self, agent):
"""Non-MiniMax providers should keep the generic probe-down behavior."""
self._setup_agent(agent)
agent.provider = "openrouter"
agent.model = "some/unknown-model"
agent.base_url = "https://openrouter.ai/api/v1"
agent.context_compressor.context_length = 200_000
agent.context_compressor.threshold_tokens = int(
agent.context_compressor.context_length * agent.context_compressor.threshold_percent
)
err_400 = Exception(
"HTTP 400: invalid params, context window exceeds limit (2013)"
)
err_400.status_code = 400
ok_resp = _mock_response(content="Recovered after compression", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [err_400, ok_resp]
prefill = [
{"role": "user", "content": "previous question"},
{"role": "assistant", "content": "previous answer"},
]
with (
patch.object(agent, "_compress_context") as mock_compress,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
mock_compress.return_value = (
[{"role": "user", "content": "hello"}],
"compressed system prompt",
)
result = agent.run_conversation("hello", conversation_history=prefill)
mock_compress.assert_called_once()
assert agent.context_compressor.context_length == 128_000
assert result["final_response"] == "Recovered after compression"
assert result["completed"] is True
def test_length_finish_reason_requests_continuation(self, agent):
"""Normal truncation (partial real content) triggers continuation."""
self._setup_agent(agent)
first = _mock_response(content="Part 1 ", finish_reason="length")
second = _mock_response(content="Part 2", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [first, second]
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
assert result["completed"] is True
assert result["api_calls"] == 2
assert result["final_response"] == "Part 1 Part 2"
second_call_messages = agent.client.chat.completions.create.call_args_list[1].kwargs["messages"]
assert second_call_messages[-1]["role"] == "user"
assert "truncated by the output length limit" in second_call_messages[-1]["content"]
def test_ollama_glm_stop_after_tools_without_terminal_boundary_requests_continuation(self, agent):
"""Ollama-hosted GLM responses can misreport truncated output as stop."""
self._setup_agent(agent)
agent.base_url = "http://localhost:11434/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "glm-5.1:cloud"
tool_turn = _mock_response(
content="",
finish_reason="tool_calls",
tool_calls=[_mock_tool_call(name="web_search", arguments="{}", call_id="c1")],
)
misreported_stop = _mock_response(
content="Based on the search results, the best next",
finish_reason="stop",
)
continued = _mock_response(
content=" step is to update the config.",
finish_reason="stop",
)
agent.client.chat.completions.create.side_effect = [
tool_turn,
misreported_stop,
continued,
]
with (
patch("run_agent.handle_function_call", return_value="search result"),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
assert result["completed"] is True
assert result["api_calls"] == 3
assert (
result["final_response"]
== "Based on the search results, the best next step is to update the config."
)
third_call_messages = agent.client.chat.completions.create.call_args_list[2].kwargs["messages"]
assert third_call_messages[-1]["role"] == "user"
assert "truncated by the output length limit" in third_call_messages[-1]["content"]
def test_ollama_glm_stop_with_terminal_boundary_does_not_continue(self, agent):
"""Complete Ollama/GLM responses should not be reclassified as truncated."""
self._setup_agent(agent)
agent.base_url = "http://localhost:11434/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "glm-5.1:cloud"
tool_turn = _mock_response(
content="",
finish_reason="tool_calls",
tool_calls=[_mock_tool_call(name="web_search", arguments="{}", call_id="c1")],
)
complete_stop = _mock_response(
content="Based on the search results, the best next step is to update the config.",
finish_reason="stop",
)
agent.client.chat.completions.create.side_effect = [tool_turn, complete_stop]
with (
patch("run_agent.handle_function_call", return_value="search result"),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
assert result["completed"] is True
assert result["api_calls"] == 2
assert (
result["final_response"]
== "Based on the search results, the best next step is to update the config."
)
def test_non_ollama_stop_without_terminal_boundary_does_not_continue(self, agent):
"""The stop->length workaround should stay scoped to Ollama/GLM backends."""
self._setup_agent(agent)
agent.base_url = "https://api.openai.com/v1"
agent._base_url_lower = agent.base_url.lower()
agent.model = "gpt-4o-mini"
tool_turn = _mock_response(
content="",
finish_reason="tool_calls",
tool_calls=[_mock_tool_call(name="web_search", arguments="{}", call_id="c1")],
)
normal_stop = _mock_response(
content="Based on the search results, the best next",
finish_reason="stop",
)
agent.client.chat.completions.create.side_effect = [tool_turn, normal_stop]
with (
patch("run_agent.handle_function_call", return_value="search result"),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
assert result["completed"] is True
assert result["api_calls"] == 2
assert result["final_response"] == "Based on the search results, the best next"
def test_length_thinking_exhausted_skips_continuation(self, agent):
"""When finish_reason='length' but content is only thinking, skip retries."""
self._setup_agent(agent)
resp = _mock_response(
content="internal reasoning",
finish_reason="length",
)
agent.client.chat.completions.create.return_value = resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
# Should return immediately — no continuation, only 1 API call
assert result["completed"] is False
assert result["api_calls"] == 1
assert "reasoning" in result["error"].lower()
assert "output tokens" in result["error"].lower()
# Should have a user-friendly response (not None)
assert result["final_response"] is not None
assert "Thinking Budget Exhausted" in result["final_response"]
assert "/thinkon" in result["final_response"]
def test_length_empty_content_without_think_tags_retries_normally(self, agent):
"""When finish_reason='length' and content is None but no think tags,
fall through to normal continuation retry (not thinking-exhaustion)."""
self._setup_agent(agent)
resp = _mock_response(content=None, finish_reason="length")
agent.client.chat.completions.create.return_value = resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("hello")
# Without think tags, the agent should attempt continuation retries
# (up to 3), not immediately fire thinking-exhaustion.
assert result["api_calls"] == 3
assert result["completed"] is False
def test_length_with_tool_calls_returns_partial_without_executing_tools(self, agent):
self._setup_agent(agent)
bad_tc = _mock_tool_call(
name="write_file",
arguments='{"path":"report.md","content":"partial',
call_id="c1",
)
resp = _mock_response(content="", finish_reason="length", tool_calls=[bad_tc])
agent.client.chat.completions.create.return_value = resp
with (
patch("run_agent.handle_function_call") as mock_handle_function_call,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("write the report")
assert result["completed"] is False
assert result["partial"] is True
assert "truncated due to output length limit" in result["error"]
mock_handle_function_call.assert_not_called()
def test_truncated_tool_call_retries_once_before_refusing(self, agent):
"""When tool call args are truncated, the agent retries the API call
once. If the retry succeeds (valid JSON args), tool execution proceeds."""
self._setup_agent(agent)
agent.valid_tool_names.add("write_file")
bad_tc = _mock_tool_call(
name="write_file",
arguments='{"path":"report.md","content":"partial',
call_id="c1",
)
truncated_resp = _mock_response(
content="", finish_reason="length", tool_calls=[bad_tc],
)
good_tc = _mock_tool_call(
name="write_file",
arguments='{"path":"report.md","content":"full content"}',
call_id="c2",
)
good_resp = _mock_response(
content="", finish_reason="stop", tool_calls=[good_tc],
)
with (
patch("run_agent.handle_function_call", return_value='{"success":true}') as mock_hfc,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
# First call: truncated → retry. Second: valid → execute tool.
# Third: final text response.
final_resp = _mock_response(content="Done!", finish_reason="stop")
agent.client.chat.completions.create.side_effect = [
truncated_resp, good_resp, final_resp,
]
result = agent.run_conversation("write the report")
# Tool was executed on the retry (good_resp)
mock_hfc.assert_called_once()
assert result["final_response"] == "Done!"
def test_truncated_tool_args_detected_when_finish_reason_not_length(self, agent):
"""When a router rewrites finish_reason from 'length' to 'tool_calls',
truncated JSON arguments should still be detected and refused rather
than wasting 3 retry attempts."""
self._setup_agent(agent)
agent.valid_tool_names.add("write_file")
bad_tc = _mock_tool_call(
name="write_file",
arguments='{"path":"report.md","content":"partial',
call_id="c1",
)
resp = _mock_response(
content="", finish_reason="tool_calls", tool_calls=[bad_tc],
)
agent.client.chat.completions.create.return_value = resp
with (
patch("run_agent.handle_function_call") as mock_handle_function_call,
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation("write the report")
assert result["completed"] is False
assert result["partial"] is True
assert "truncated due to output length limit" in result["error"]
mock_handle_function_call.assert_not_called()
class TestRetryExhaustion:
"""Regression: retry_count > max_retries was dead code (off-by-one).
When retries were exhausted the condition never triggered, causing
the loop to exit and fall through to response.choices[0] on an
invalid response, raising IndexError.
"""
def _setup_agent(self, agent):
agent._cached_system_prompt = "You are helpful."
agent._use_prompt_caching = False
agent.tool_delay = 0
agent.compression_enabled = False
agent.save_trajectories = False
@staticmethod
def _make_fast_time_mock():
"""Return a mock time module where sleep loops exit instantly."""
mock_time = MagicMock()
_t = [1000.0]
def _advancing_time():
_t[0] += 500.0 # jump 500s per call so sleep_end is always in the past
return _t[0]
mock_time.time.side_effect = _advancing_time
mock_time.sleep = MagicMock() # no-op
mock_time.monotonic.return_value = 12345.0
return mock_time
def test_invalid_response_returns_error_not_crash(self, agent):
"""Exhausted retries on invalid (empty choices) response must not IndexError."""
self._setup_agent(agent)
# Return response with empty choices every time
bad_resp = SimpleNamespace(
choices=[],
model="test/model",
usage=None,
)
agent.client.chat.completions.create.return_value = bad_resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch("run_agent.time", self._make_fast_time_mock()),
):
result = agent.run_conversation("hello")
assert result.get("completed") is False, (
f"Expected completed=False, got: {result}"
)
assert result.get("failed") is True
assert "error" in result
assert "Invalid API response" in result["error"]
def test_api_error_returns_gracefully_after_retries(self, agent):
"""Exhausted retries on API errors must return error result, not crash."""
self._setup_agent(agent)
agent.client.chat.completions.create.side_effect = RuntimeError("rate limited")
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch("run_agent.time", self._make_fast_time_mock()),
):
result = agent.run_conversation("hello")
assert result.get("completed") is False
assert result.get("failed") is True
assert "error" in result
assert "rate limited" in result["error"]
def test_build_api_kwargs_error_no_unbound_local(self, agent):
"""When _build_api_kwargs raises, except handler must not crash with UnboundLocalError.
Regression: _dump_api_request_debug(api_kwargs, ...) in the except block
referenced api_kwargs before it was assigned when _build_api_kwargs threw.
"""
self._setup_agent(agent)
with (
patch.object(agent, "_build_api_kwargs", side_effect=ValueError("bad messages")),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch("run_agent.time", self._make_fast_time_mock()),
):
result = agent.run_conversation("hello")
# Must surface the real error, not UnboundLocalError
assert result.get("completed") is False
assert result.get("failed") is True
assert "error" in result
assert "UnboundLocalError" not in result.get("error", "")
assert "bad messages" in result["error"]
# ---------------------------------------------------------------------------
# Conversation history mutation
# ---------------------------------------------------------------------------
class TestConversationHistoryNotMutated:
"""run_conversation must not mutate the caller's conversation_history list."""
def test_caller_list_unchanged_after_run(self, agent):
"""Passing conversation_history should not modify the original list."""
history = [
{"role": "user", "content": "previous question"},
{"role": "assistant", "content": "previous answer"},
]
original_len = len(history)
resp = _mock_response(content="new answer", finish_reason="stop")
agent.client.chat.completions.create.return_value = resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation(
"new question", conversation_history=history
)
# Caller's list must be untouched
assert len(history) == original_len, (
f"conversation_history was mutated: expected {original_len} items, got {len(history)}"
)
# Result should have more messages than the original history
assert len(result["messages"]) > original_len
# ---------------------------------------------------------------------------
# _max_tokens_param consistency
# ---------------------------------------------------------------------------
class TestNousCredentialRefresh:
"""Verify Nous credential refresh rebuilds the runtime client."""
def test_try_refresh_nous_client_credentials_rebuilds_client(
self, agent, monkeypatch
):
agent.provider = "nous"
agent.api_mode = "chat_completions"
closed = {"value": False}
rebuilt = {"kwargs": None}
captured = {}
class _ExistingClient:
def close(self):
closed["value"] = True
class _RebuiltClient:
pass
def _fake_resolve(**kwargs):
captured.update(kwargs)
return {
"api_key": "new-nous-key",
"base_url": "https://inference-api.nousresearch.com/v1",
}
def _fake_openai(**kwargs):
rebuilt["kwargs"] = kwargs
return _RebuiltClient()
monkeypatch.setattr(
"hermes_cli.auth.resolve_nous_runtime_credentials", _fake_resolve
)
agent.client = _ExistingClient()
with patch("run_agent.OpenAI", side_effect=_fake_openai):
ok = agent._try_refresh_nous_client_credentials(force=True)
assert ok is True
assert closed["value"] is True
assert captured["force_mint"] is True
assert rebuilt["kwargs"]["api_key"] == "new-nous-key"
assert (
rebuilt["kwargs"]["base_url"] == "https://inference-api.nousresearch.com/v1"
)
assert "default_headers" not in rebuilt["kwargs"]
assert isinstance(agent.client, _RebuiltClient)
class TestCredentialPoolRecovery:
def test_recover_with_pool_rotates_on_402(self, agent):
current = SimpleNamespace(label="primary")
next_entry = SimpleNamespace(label="secondary")
class _Pool:
def current(self):
return current
def mark_exhausted_and_rotate(self, *, status_code, error_context=None):
assert status_code == 402
assert error_context is None
return next_entry
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=402,
has_retried_429=False,
)
assert recovered is True
assert retry_same is False
agent._swap_credential.assert_called_once_with(next_entry)
def test_recover_with_pool_rotates_on_billing_reason_even_with_http_400(self, agent):
next_entry = SimpleNamespace(label="secondary")
class _Pool:
def mark_exhausted_and_rotate(self, *, status_code, error_context=None):
assert status_code == 400
assert error_context == {"reason": "out_of_extra_usage"}
return next_entry
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=400,
has_retried_429=False,
classified_reason=FailoverReason.billing,
error_context={"reason": "out_of_extra_usage"},
)
assert recovered is True
assert retry_same is False
agent._swap_credential.assert_called_once_with(next_entry)
def test_recover_with_pool_retries_first_429_then_rotates(self, agent):
next_entry = SimpleNamespace(label="secondary")
class _Pool:
def current(self):
return SimpleNamespace(label="primary")
def mark_exhausted_and_rotate(self, *, status_code, error_context=None):
assert status_code == 429
assert error_context is None
return next_entry
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=429,
has_retried_429=False,
)
assert recovered is False
assert retry_same is True
agent._swap_credential.assert_not_called()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=429,
has_retried_429=True,
)
assert recovered is True
assert retry_same is False
agent._swap_credential.assert_called_once_with(next_entry)
def test_recover_with_pool_refreshes_on_401(self, agent):
"""401 with successful refresh should swap to refreshed credential."""
refreshed_entry = SimpleNamespace(label="refreshed-primary", id="abc")
class _Pool:
def try_refresh_current(self):
return refreshed_entry
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=401,
has_retried_429=False,
)
assert recovered is True
agent._swap_credential.assert_called_once_with(refreshed_entry)
def test_recover_with_pool_rotates_on_401_when_refresh_fails(self, agent):
"""401 with failed refresh should rotate to next credential."""
next_entry = SimpleNamespace(label="secondary", id="def")
class _Pool:
def try_refresh_current(self):
return None # refresh failed
def mark_exhausted_and_rotate(self, *, status_code, error_context=None):
assert status_code == 401
assert error_context is None
return next_entry
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=401,
has_retried_429=False,
)
assert recovered is True
assert retry_same is False
agent._swap_credential.assert_called_once_with(next_entry)
def test_recover_with_pool_401_refresh_fails_no_more_credentials(self, agent):
"""401 with failed refresh and no other credentials returns not recovered."""
class _Pool:
def try_refresh_current(self):
return None
def mark_exhausted_and_rotate(self, *, status_code, error_context=None):
assert error_context is None
return None # no more credentials
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=401,
has_retried_429=False,
)
assert recovered is False
agent._swap_credential.assert_not_called()
def test_extract_api_error_context_uses_reset_timestamp_and_reason(self, agent):
response = SimpleNamespace(headers={})
error = SimpleNamespace(
body={
"error": {
"code": "device_code_exhausted",
"message": "Weekly credits exhausted.",
"resets_at": "2026-04-12T10:30:00Z",
}
},
response=response,
)
context = agent._extract_api_error_context(error)
assert context["reason"] == "device_code_exhausted"
assert context["message"] == "Weekly credits exhausted."
assert context["reset_at"] == "2026-04-12T10:30:00Z"
def test_recover_with_pool_passes_error_context_on_rotated_429(self, agent):
next_entry = SimpleNamespace(label="secondary")
captured = {}
class _Pool:
def current(self):
return SimpleNamespace(label="primary")
def mark_exhausted_and_rotate(self, *, status_code, error_context=None):
captured["status_code"] = status_code
captured["error_context"] = error_context
return next_entry
agent._credential_pool = _Pool()
agent._swap_credential = MagicMock()
recovered, retry_same = agent._recover_with_credential_pool(
status_code=429,
has_retried_429=True,
error_context={"reason": "device_code_exhausted", "reset_at": "2026-04-12T10:30:00Z"},
)
assert recovered is True
assert retry_same is False
assert captured["status_code"] == 429
assert captured["error_context"]["reason"] == "device_code_exhausted"
class TestMaxTokensParam:
"""Verify _max_tokens_param returns the correct key for each provider."""
def test_returns_max_completion_tokens_for_direct_openai(self, agent):
agent.base_url = "https://api.openai.com/v1"
result = agent._max_tokens_param(4096)
assert result == {"max_completion_tokens": 4096}
def test_returns_max_tokens_for_openrouter(self, agent):
agent.base_url = "https://openrouter.ai/api/v1"
result = agent._max_tokens_param(4096)
assert result == {"max_tokens": 4096}
def test_returns_max_tokens_for_local(self, agent):
agent.base_url = "http://localhost:11434/v1"
result = agent._max_tokens_param(4096)
assert result == {"max_tokens": 4096}
def test_not_tricked_by_openai_in_openrouter_url(self, agent):
agent.base_url = "https://openrouter.ai/api/v1/api.openai.com"
result = agent._max_tokens_param(4096)
assert result == {"max_tokens": 4096}
def test_returns_max_completion_tokens_for_azure(self, agent):
"""Azure OpenAI requires max_completion_tokens for gpt-5.x models."""
agent.base_url = "https://my-resource.openai.azure.com/openai/v1"
result = agent._max_tokens_param(4096)
assert result == {"max_completion_tokens": 4096}
class TestAzureOpenAIRouting:
"""Verify Azure OpenAI endpoints stay on chat_completions for gpt-5.x."""
def test_azure_gpt5_stays_on_chat_completions(self, agent):
"""Azure serves gpt-5.x on /chat/completions — must not upgrade to codex_responses."""
agent.base_url = "https://my-resource.openai.azure.com/openai/v1"
agent.api_mode = "chat_completions"
agent.model = "gpt-5.4-mini"
# Mirror the routing logic from __init__
if (
agent.api_mode == "chat_completions"
and not agent._is_azure_openai_url()
and (
agent._is_direct_openai_url()
or agent._provider_model_requires_responses_api(
agent.model, provider=agent.provider,
)
)
):
agent.api_mode = "codex_responses"
assert agent.api_mode == "chat_completions"
def test_non_azure_gpt5_upgrades_to_codex_responses(self, agent):
"""On api.openai.com, gpt-5.x must still upgrade to codex_responses."""
agent.base_url = "https://api.openai.com/v1"
agent.api_mode = "chat_completions"
agent.model = "gpt-5.4-mini"
if (
agent.api_mode == "chat_completions"
and not agent._is_azure_openai_url()
and (
agent._is_direct_openai_url()
or agent._provider_model_requires_responses_api(
agent.model, provider=agent.provider,
)
)
):
agent.api_mode = "codex_responses"
assert agent.api_mode == "codex_responses"
def test_is_azure_openai_url_detection(self, agent):
assert agent._is_azure_openai_url("https://foo.openai.azure.com/openai/v1") is True
assert agent._is_azure_openai_url("https://api.openai.com/v1") is False
assert agent._is_azure_openai_url("https://openrouter.ai/api/v1") is False
# Path-embedded azure string should still detect — we're ~substring matching
agent.base_url = "https://my-resource.openai.azure.com/openai/v1"
assert agent._is_azure_openai_url() is True
# ---------------------------------------------------------------------------
# System prompt stability for prompt caching
# ---------------------------------------------------------------------------
class TestSystemPromptStability:
"""Verify that the system prompt stays stable across turns for cache hits."""
def test_stored_prompt_reused_for_continuing_session(self, agent):
"""When conversation_history is non-empty and session DB has a stored
prompt, it should be reused instead of rebuilding from disk."""
stored = "You are helpful. [stored from turn 1]"
mock_db = MagicMock()
mock_db.get_session.return_value = {"system_prompt": stored}
agent._session_db = mock_db
# Simulate a continuing session with history
history = [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi"},
]
# First call — _cached_system_prompt is None, history is non-empty
agent._cached_system_prompt = None
# Patch run_conversation internals to just test the system prompt logic.
# We'll call the prompt caching block directly by simulating what
# run_conversation does.
conversation_history = history
# The block under test (from run_conversation):
if agent._cached_system_prompt is None:
stored_prompt = None
if conversation_history and agent._session_db:
try:
session_row = agent._session_db.get_session(agent.session_id)
if session_row:
stored_prompt = session_row.get("system_prompt") or None
except Exception:
pass
if stored_prompt:
agent._cached_system_prompt = stored_prompt
assert agent._cached_system_prompt == stored
mock_db.get_session.assert_called_once_with(agent.session_id)
def test_fresh_build_when_no_history(self, agent):
"""On the first turn (no history), system prompt should be built fresh."""
mock_db = MagicMock()
agent._session_db = mock_db
agent._cached_system_prompt = None
conversation_history = []
# The block under test:
if agent._cached_system_prompt is None:
stored_prompt = None
if conversation_history and agent._session_db:
session_row = agent._session_db.get_session(agent.session_id)
if session_row:
stored_prompt = session_row.get("system_prompt") or None
if stored_prompt:
agent._cached_system_prompt = stored_prompt
else:
agent._cached_system_prompt = agent._build_system_prompt()
# Should have built fresh, not queried the DB
mock_db.get_session.assert_not_called()
assert agent._cached_system_prompt is not None
assert "Hermes Agent" in agent._cached_system_prompt
def test_fresh_build_when_db_has_no_prompt(self, agent):
"""If the session DB has no stored prompt, build fresh even with history."""
mock_db = MagicMock()
mock_db.get_session.return_value = {"system_prompt": ""}
agent._session_db = mock_db
agent._cached_system_prompt = None
conversation_history = [{"role": "user", "content": "hi"}]
if agent._cached_system_prompt is None:
stored_prompt = None
if conversation_history and agent._session_db:
try:
session_row = agent._session_db.get_session(agent.session_id)
if session_row:
stored_prompt = session_row.get("system_prompt") or None
except Exception:
pass
if stored_prompt:
agent._cached_system_prompt = stored_prompt
else:
agent._cached_system_prompt = agent._build_system_prompt()
# Empty string is falsy, so should fall through to fresh build
assert "Hermes Agent" in agent._cached_system_prompt
class TestBudgetPressure:
"""Budget exhaustion grace call system."""
def test_grace_call_flags_initialized(self, agent):
"""Agent should have budget grace call flags."""
assert agent._budget_exhausted_injected is False
assert agent._budget_grace_call is False
class TestSafeWriter:
"""Verify _SafeWriter guards stdout against OSError (broken pipes)."""
def test_write_delegates_normally(self):
"""When stdout is healthy, _SafeWriter is transparent."""
from run_agent import _SafeWriter
from io import StringIO
inner = StringIO()
writer = _SafeWriter(inner)
writer.write("hello")
assert inner.getvalue() == "hello"
def test_write_catches_oserror(self):
"""OSError on write is silently caught, returns len(data)."""
from run_agent import _SafeWriter
from unittest.mock import MagicMock
inner = MagicMock()
inner.write.side_effect = OSError(5, "Input/output error")
writer = _SafeWriter(inner)
result = writer.write("hello")
assert result == 5 # len("hello")
def test_flush_catches_oserror(self):
"""OSError on flush is silently caught."""
from run_agent import _SafeWriter
from unittest.mock import MagicMock
inner = MagicMock()
inner.flush.side_effect = OSError(5, "Input/output error")
writer = _SafeWriter(inner)
writer.flush() # should not raise
def test_print_survives_broken_stdout(self, monkeypatch):
"""print() through _SafeWriter doesn't crash on broken pipe."""
import sys
from run_agent import _SafeWriter
from unittest.mock import MagicMock
broken = MagicMock()
broken.write.side_effect = OSError(5, "Input/output error")
original = sys.stdout
sys.stdout = _SafeWriter(broken)
try:
print("this should not crash") # would raise without _SafeWriter
finally:
sys.stdout = original
def test_installed_in_run_conversation(self, agent):
"""run_conversation installs _SafeWriter on stdio."""
import sys
from run_agent import _SafeWriter
resp = _mock_response(content="Done", finish_reason="stop")
agent.client.chat.completions.create.return_value = resp
original_stdout = sys.stdout
original_stderr = sys.stderr
try:
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
agent.run_conversation("test")
assert isinstance(sys.stdout, _SafeWriter)
assert isinstance(sys.stderr, _SafeWriter)
finally:
sys.stdout = original_stdout
sys.stderr = original_stderr
# test_installed_before_init_time_honcho_error_prints removed —
# Honcho integration extracted to plugin (PR #4154).
def test_double_wrap_prevented(self):
"""Wrapping an already-wrapped stream doesn't add layers."""
import sys
from run_agent import _SafeWriter
from io import StringIO
inner = StringIO()
wrapped = _SafeWriter(inner)
# isinstance check should prevent double-wrapping
assert isinstance(wrapped, _SafeWriter)
# The guard in run_conversation checks isinstance before wrapping
if not isinstance(wrapped, _SafeWriter):
wrapped = _SafeWriter(wrapped)
# Still just one layer
wrapped.write("test")
assert inner.getvalue() == "test"
class TestSaveSessionLogAtomicWrite:
def test_uses_shared_atomic_json_helper(self, agent, tmp_path):
agent.session_log_file = tmp_path / "session.json"
messages = [{"role": "user", "content": "hello"}]
with patch("run_agent.atomic_json_write", create=True) as mock_atomic_write:
agent._save_session_log(messages)
mock_atomic_write.assert_called_once()
call_args = mock_atomic_write.call_args
assert call_args.args[0] == agent.session_log_file
payload = call_args.args[1]
assert payload["session_id"] == agent.session_id
assert payload["messages"] == messages
assert call_args.kwargs["indent"] == 2
assert call_args.kwargs["default"] is str
# ===================================================================
# Anthropic adapter integration fixes
# ===================================================================
class TestBuildApiKwargsAnthropicMaxTokens:
"""Bug fix: max_tokens was always None for Anthropic mode, ignoring user config."""
def test_max_tokens_passed_to_anthropic(self, agent):
agent.api_mode = "anthropic_messages"
agent.max_tokens = 4096
agent.reasoning_config = None
with patch("agent.anthropic_adapter.build_anthropic_kwargs") as mock_build:
mock_build.return_value = {"model": "claude-sonnet-4-20250514", "messages": [], "max_tokens": 4096}
agent._build_api_kwargs([{"role": "user", "content": "test"}])
_, kwargs = mock_build.call_args
if not kwargs:
kwargs = dict(zip(
["model", "messages", "tools", "max_tokens", "reasoning_config"],
mock_build.call_args[0],
))
assert kwargs.get("max_tokens") == 4096 or mock_build.call_args[1].get("max_tokens") == 4096
def test_max_tokens_none_when_unset(self, agent):
agent.api_mode = "anthropic_messages"
agent.max_tokens = None
agent.reasoning_config = None
with patch("agent.anthropic_adapter.build_anthropic_kwargs") as mock_build:
mock_build.return_value = {"model": "claude-sonnet-4-20250514", "messages": [], "max_tokens": 16384}
agent._build_api_kwargs([{"role": "user", "content": "test"}])
call_args = mock_build.call_args
# max_tokens should be None (let adapter use its default)
if call_args[1]:
assert call_args[1].get("max_tokens") is None
else:
assert call_args[0][3] is None
class TestAnthropicImageFallback:
def test_build_api_kwargs_converts_multimodal_user_image_to_text(self, agent):
agent.api_mode = "anthropic_messages"
agent.reasoning_config = None
api_messages = [{
"role": "user",
"content": [
{"type": "text", "text": "Can you see this now?"},
{"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}},
],
}]
with (
patch("tools.vision_tools.vision_analyze_tool", new=AsyncMock(return_value=json.dumps({"success": True, "analysis": "A cat sitting on a chair."}))),
patch("agent.anthropic_adapter.build_anthropic_kwargs") as mock_build,
):
mock_build.return_value = {"model": "claude-sonnet-4-20250514", "messages": [], "max_tokens": 4096}
agent._build_api_kwargs(api_messages)
kwargs = mock_build.call_args.kwargs or dict(zip(
["model", "messages", "tools", "max_tokens", "reasoning_config"],
mock_build.call_args.args,
))
transformed = kwargs["messages"]
assert isinstance(transformed[0]["content"], str)
assert "A cat sitting on a chair." in transformed[0]["content"]
assert "Can you see this now?" in transformed[0]["content"]
assert "vision_analyze with image_url: https://example.com/cat.png" in transformed[0]["content"]
def test_build_api_kwargs_reuses_cached_image_analysis_for_duplicate_images(self, agent):
agent.api_mode = "anthropic_messages"
agent.reasoning_config = None
data_url = "data:image/png;base64,QUFBQQ=="
api_messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "first"},
{"type": "input_image", "image_url": data_url},
],
},
{
"role": "user",
"content": [
{"type": "text", "text": "second"},
{"type": "input_image", "image_url": data_url},
],
},
]
mock_vision = AsyncMock(return_value=json.dumps({"success": True, "analysis": "A small test image."}))
with (
patch("tools.vision_tools.vision_analyze_tool", new=mock_vision),
patch("agent.anthropic_adapter.build_anthropic_kwargs") as mock_build,
):
mock_build.return_value = {"model": "claude-sonnet-4-20250514", "messages": [], "max_tokens": 4096}
agent._build_api_kwargs(api_messages)
assert mock_vision.await_count == 1
class TestFallbackAnthropicProvider:
"""Bug fix: _try_activate_fallback had no case for anthropic provider."""
def test_fallback_to_anthropic_sets_api_mode(self, agent):
agent._fallback_activated = False
agent._fallback_model = {"provider": "anthropic", "model": "claude-sonnet-4-20250514"}
agent._fallback_chain = [agent._fallback_model]
agent._fallback_index = 0
mock_client = MagicMock()
mock_client.base_url = "https://api.anthropic.com/v1"
mock_client.api_key = "sk-ant-api03-test"
with (
patch("agent.auxiliary_client.resolve_provider_client", return_value=(mock_client, None)),
patch("agent.anthropic_adapter.build_anthropic_client") as mock_build,
patch("agent.anthropic_adapter.resolve_anthropic_token", return_value=None),
):
mock_build.return_value = MagicMock()
result = agent._try_activate_fallback()
assert result is True
assert agent.api_mode == "anthropic_messages"
assert agent._anthropic_client is not None
assert agent.client is None
def test_fallback_to_anthropic_enables_prompt_caching(self, agent):
agent._fallback_activated = False
agent._fallback_model = {"provider": "anthropic", "model": "claude-sonnet-4-20250514"}
agent._fallback_chain = [agent._fallback_model]
agent._fallback_index = 0
mock_client = MagicMock()
mock_client.base_url = "https://api.anthropic.com/v1"
mock_client.api_key = "sk-ant-api03-test"
with (
patch("agent.auxiliary_client.resolve_provider_client", return_value=(mock_client, None)),
patch("agent.anthropic_adapter.build_anthropic_client", return_value=MagicMock()),
patch("agent.anthropic_adapter.resolve_anthropic_token", return_value=None),
):
agent._try_activate_fallback()
assert agent._use_prompt_caching is True
def test_fallback_to_openrouter_uses_openai_client(self, agent):
agent._fallback_activated = False
agent._fallback_model = {"provider": "openrouter", "model": "anthropic/claude-sonnet-4"}
agent._fallback_chain = [agent._fallback_model]
agent._fallback_index = 0
mock_client = MagicMock()
mock_client.base_url = "https://openrouter.ai/api/v1"
mock_client.api_key = "sk-or-test"
with patch("agent.auxiliary_client.resolve_provider_client", return_value=(mock_client, None)):
result = agent._try_activate_fallback()
assert result is True
assert agent.api_mode == "chat_completions"
assert agent.client is mock_client
def test_aiagent_uses_copilot_acp_client():
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("run_agent.OpenAI") as mock_openai,
patch("agent.copilot_acp_client.CopilotACPClient") as mock_acp_client,
):
acp_client = MagicMock()
mock_acp_client.return_value = acp_client
agent = AIAgent(
api_key="copilot-acp",
base_url="acp://copilot",
provider="copilot-acp",
acp_command="/usr/local/bin/copilot",
acp_args=["--acp", "--stdio"],
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
assert agent.client is acp_client
mock_openai.assert_not_called()
mock_acp_client.assert_called_once()
assert mock_acp_client.call_args.kwargs["base_url"] == "acp://copilot"
assert mock_acp_client.call_args.kwargs["api_key"] == "copilot-acp"
assert mock_acp_client.call_args.kwargs["command"] == "/usr/local/bin/copilot"
assert mock_acp_client.call_args.kwargs["args"] == ["--acp", "--stdio"]
def test_quiet_spinner_allowed_with_explicit_print_fn(agent):
agent._print_fn = lambda *_a, **_kw: None
with patch.object(run_agent.sys.stdout, "isatty", return_value=False):
assert agent._should_start_quiet_spinner() is True
def test_quiet_spinner_allowed_on_real_tty(agent):
agent._print_fn = None
with patch.object(run_agent.sys.stdout, "isatty", return_value=True):
assert agent._should_start_quiet_spinner() is True
def test_quiet_spinner_suppressed_on_non_tty_without_print_fn(agent):
agent._print_fn = None
with patch.object(run_agent.sys.stdout, "isatty", return_value=False):
assert agent._should_start_quiet_spinner() is False
def test_is_openai_client_closed_honors_custom_client_flag():
assert AIAgent._is_openai_client_closed(SimpleNamespace(is_closed=True)) is True
assert AIAgent._is_openai_client_closed(SimpleNamespace(is_closed=False)) is False
def test_is_openai_client_closed_handles_method_form():
"""Fix for issue #4377: is_closed as method (openai SDK) vs property (httpx).
The openai SDK's is_closed is a method, not a property. Prior to this fix,
getattr(client, "is_closed", False) returned the bound method object, which
is always truthy, causing the function to incorrectly report all clients as
closed and triggering unnecessary client recreation on every API call.
"""
class MethodFormClient:
"""Mimics openai.OpenAI where is_closed() is a method."""
def __init__(self, closed: bool):
self._closed = closed
def is_closed(self) -> bool:
return self._closed
# Method returning False - client is open
open_client = MethodFormClient(closed=False)
assert AIAgent._is_openai_client_closed(open_client) is False
# Method returning True - client is closed
closed_client = MethodFormClient(closed=True)
assert AIAgent._is_openai_client_closed(closed_client) is True
def test_is_openai_client_closed_falls_back_to_http_client():
"""Verify fallback to _client.is_closed when top-level is_closed is None."""
class ClientWithHttpClient:
is_closed = None # No top-level is_closed
def __init__(self, http_closed: bool):
self._client = SimpleNamespace(is_closed=http_closed)
assert AIAgent._is_openai_client_closed(ClientWithHttpClient(http_closed=False)) is False
assert AIAgent._is_openai_client_closed(ClientWithHttpClient(http_closed=True)) is True
class TestAnthropicBaseUrlPassthrough:
"""Bug fix: base_url was filtered with 'anthropic in base_url', blocking proxies."""
def test_custom_proxy_base_url_passed_through(self):
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter.build_anthropic_client") as mock_build,
):
mock_build.return_value = MagicMock()
a = AIAgent(
api_key="sk-ant-api03-test1234567890",
base_url="https://llm-proxy.company.com/v1",
api_mode="anthropic_messages",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
call_args = mock_build.call_args
# base_url should be passed through, not filtered out
assert call_args[0][1] == "https://llm-proxy.company.com/v1"
def test_none_base_url_passed_as_none(self):
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter.build_anthropic_client") as mock_build,
):
mock_build.return_value = MagicMock()
a = AIAgent(
api_key="sk-ant...7890",
api_mode="anthropic_messages",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
call_args = mock_build.call_args
# No base_url provided, should be default empty string or None
passed_url = call_args[0][1]
assert not passed_url or passed_url is None
class TestAnthropicCredentialRefresh:
def test_try_refresh_anthropic_client_credentials_rebuilds_client(self):
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter.build_anthropic_client") as mock_build,
):
old_client = MagicMock()
new_client = MagicMock()
mock_build.side_effect = [old_client, new_client]
agent = AIAgent(
api_key="sk-ant-oat01-stale-token",
base_url="https://openrouter.ai/api/v1",
api_mode="anthropic_messages",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
agent._anthropic_client = old_client
agent._anthropic_api_key = "sk-ant-oat01-stale-token"
agent._anthropic_base_url = "https://api.anthropic.com"
agent.provider = "anthropic"
with (
patch("agent.anthropic_adapter.resolve_anthropic_token", return_value="sk-ant-oat01-fresh-token"),
patch("agent.anthropic_adapter.build_anthropic_client", return_value=new_client) as rebuild,
):
assert agent._try_refresh_anthropic_client_credentials() is True
old_client.close.assert_called_once()
rebuild.assert_called_once_with(
"sk-ant-oat01-fresh-token", "https://api.anthropic.com", timeout=None,
)
assert agent._anthropic_client is new_client
assert agent._anthropic_api_key == "sk-ant-oat01-fresh-token"
def test_try_refresh_anthropic_client_credentials_returns_false_when_token_unchanged(self):
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter.build_anthropic_client", return_value=MagicMock()),
):
agent = AIAgent(
api_key="sk-ant-oat01-same-token",
base_url="https://openrouter.ai/api/v1",
api_mode="anthropic_messages",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
old_client = MagicMock()
agent._anthropic_client = old_client
agent._anthropic_api_key = "sk-ant-oat01-same-token"
with (
patch("agent.anthropic_adapter.resolve_anthropic_token", return_value="sk-ant-oat01-same-token"),
patch("agent.anthropic_adapter.build_anthropic_client") as rebuild,
):
assert agent._try_refresh_anthropic_client_credentials() is False
old_client.close.assert_not_called()
rebuild.assert_not_called()
def test_anthropic_messages_create_preflights_refresh(self):
with (
patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
patch("run_agent.check_toolset_requirements", return_value={}),
patch("agent.anthropic_adapter.build_anthropic_client", return_value=MagicMock()),
):
agent = AIAgent(
api_key="sk-ant-oat01-current-token",
base_url="https://openrouter.ai/api/v1",
api_mode="anthropic_messages",
quiet_mode=True,
skip_context_files=True,
skip_memory=True,
)
response = SimpleNamespace(content=[])
agent._anthropic_client = MagicMock()
agent._anthropic_client.messages.create.return_value = response
with patch.object(agent, "_try_refresh_anthropic_client_credentials", return_value=True) as refresh:
result = agent._anthropic_messages_create({"model": "claude-sonnet-4-20250514"})
refresh.assert_called_once_with()
agent._anthropic_client.messages.create.assert_called_once_with(model="claude-sonnet-4-20250514")
assert result is response
# ===================================================================
# _streaming_api_call tests
# ===================================================================
def _make_chunk(content=None, tool_calls=None, finish_reason=None, model="test/model"):
"""Build a SimpleNamespace mimicking an OpenAI streaming chunk."""
delta = SimpleNamespace(content=content, tool_calls=tool_calls)
choice = SimpleNamespace(delta=delta, finish_reason=finish_reason)
return SimpleNamespace(model=model, choices=[choice])
def _make_tc_delta(index=0, tc_id=None, name=None, arguments=None):
"""Build a SimpleNamespace mimicking a streaming tool_call delta."""
func = SimpleNamespace(name=name, arguments=arguments)
return SimpleNamespace(index=index, id=tc_id, function=func)
class TestStreamingApiCall:
"""Tests for _streaming_api_call — voice TTS streaming pipeline."""
def test_content_assembly(self, agent):
chunks = [
_make_chunk(content="Hel"),
_make_chunk(content="lo "),
_make_chunk(content="World"),
_make_chunk(finish_reason="stop"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
callback = MagicMock()
agent.stream_delta_callback = callback
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.choices[0].message.content == "Hello World"
assert resp.choices[0].finish_reason == "stop"
assert callback.call_count == 3
callback.assert_any_call("Hel")
callback.assert_any_call("lo ")
callback.assert_any_call("World")
def test_tool_call_accumulation(self, agent):
# Per OpenAI streaming spec, function names are delivered atomically
# in the first chunk; only `arguments` is fragmented across chunks.
# The accumulator uses assignment for names (immune to MiniMax/NIM
# resends of the full name) and `+=` for arguments.
chunks = [
_make_chunk(tool_calls=[_make_tc_delta(0, "call_1", "web_search", '{"q":')]),
_make_chunk(tool_calls=[_make_tc_delta(0, None, None, '"test"}')]),
_make_chunk(finish_reason="tool_calls"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
tc = resp.choices[0].message.tool_calls
assert len(tc) == 1
assert tc[0].function.name == "web_search"
assert tc[0].function.arguments == '{"q":"test"}'
assert tc[0].id == "call_1"
def test_multiple_tool_calls(self, agent):
chunks = [
_make_chunk(tool_calls=[_make_tc_delta(0, "call_a", "search", '{}')]),
_make_chunk(tool_calls=[_make_tc_delta(1, "call_b", "read", '{}')]),
_make_chunk(finish_reason="tool_calls"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
tc = resp.choices[0].message.tool_calls
assert len(tc) == 2
assert tc[0].function.name == "search"
assert tc[1].function.name == "read"
def test_truncated_tool_call_args_upgrade_finish_reason_to_length(self, agent):
chunks = [
_make_chunk(tool_calls=[_make_tc_delta(0, "call_1", "write_file", '{"path":"x.txt","content":"hel')]),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
tc = resp.choices[0].message.tool_calls
assert len(tc) == 1
assert tc[0].function.name == "write_file"
assert tc[0].function.arguments == '{"path":"x.txt","content":"hel'
assert resp.choices[0].finish_reason == "length"
def test_ollama_reused_index_separate_tool_calls(self, agent):
"""Ollama sends every tool call at index 0 with different ids.
Without the fix, names and arguments get concatenated into one slot.
"""
chunks = [
_make_chunk(tool_calls=[_make_tc_delta(0, "call_a", "search", '{"q":"hello"}')]),
# Second tool call at the SAME index 0, but different id
_make_chunk(tool_calls=[_make_tc_delta(0, "call_b", "read_file", '{"path":"x.py"}')]),
_make_chunk(finish_reason="tool_calls"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
tc = resp.choices[0].message.tool_calls
assert len(tc) == 2, f"Expected 2 tool calls, got {len(tc)}: {[t.function.name for t in tc]}"
assert tc[0].function.name == "search"
assert tc[0].function.arguments == '{"q":"hello"}'
assert tc[0].id == "call_a"
assert tc[1].function.name == "read_file"
assert tc[1].function.arguments == '{"path":"x.py"}'
assert tc[1].id == "call_b"
def test_ollama_reused_index_streamed_args(self, agent):
"""Ollama with streamed arguments across multiple chunks at same index."""
chunks = [
_make_chunk(tool_calls=[_make_tc_delta(0, "call_a", "search", '{"q":')]),
_make_chunk(tool_calls=[_make_tc_delta(0, None, None, '"hello"}')]),
# New tool call, same index 0
_make_chunk(tool_calls=[_make_tc_delta(0, "call_b", "read", '{}')]),
_make_chunk(finish_reason="tool_calls"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
tc = resp.choices[0].message.tool_calls
assert len(tc) == 2
assert tc[0].function.name == "search"
assert tc[0].function.arguments == '{"q":"hello"}'
assert tc[1].function.name == "read"
assert tc[1].function.arguments == '{}'
def test_content_and_tool_calls_together(self, agent):
chunks = [
_make_chunk(content="I'll search"),
_make_chunk(tool_calls=[_make_tc_delta(0, "call_1", "search", '{}')]),
_make_chunk(finish_reason="tool_calls"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.choices[0].message.content == "I'll search"
assert len(resp.choices[0].message.tool_calls) == 1
def test_empty_content_returns_none(self, agent):
chunks = [_make_chunk(finish_reason="stop")]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.choices[0].message.content is None
assert resp.choices[0].message.tool_calls is None
def test_callback_exception_swallowed(self, agent):
chunks = [
_make_chunk(content="Hello"),
_make_chunk(content=" World"),
_make_chunk(finish_reason="stop"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
agent.stream_delta_callback = MagicMock(side_effect=ValueError("boom"))
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.choices[0].message.content == "Hello World"
def test_model_name_captured(self, agent):
chunks = [
_make_chunk(content="Hi", model="gpt-4o"),
_make_chunk(finish_reason="stop", model="gpt-4o"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.model == "gpt-4o"
def test_stream_kwarg_injected(self, agent):
chunks = [_make_chunk(content="x"), _make_chunk(finish_reason="stop")]
agent.client.chat.completions.create.return_value = iter(chunks)
agent._interruptible_streaming_api_call({"messages": [], "model": "test"})
call_kwargs = agent.client.chat.completions.create.call_args
assert call_kwargs[1].get("stream") is True or call_kwargs.kwargs.get("stream") is True
def test_api_exception_propagates_no_non_streaming_fallback(self, agent):
"""When streaming fails before any deltas, error propagates to the main retry loop."""
agent.client.chat.completions.create.side_effect = ConnectionError("fail")
# Prevent stream retry logic from replacing the mock client
with patch.object(agent, "_replace_primary_openai_client", return_value=False):
# The fallback also uses the same client, so it'll fail too
with pytest.raises(ConnectionError, match="fail"):
agent._interruptible_streaming_api_call({"messages": []})
def test_response_has_uuid_id(self, agent):
chunks = [_make_chunk(content="x"), _make_chunk(finish_reason="stop")]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.id.startswith("stream-")
assert len(resp.id) > len("stream-")
def test_empty_choices_chunk_skipped(self, agent):
empty_chunk = SimpleNamespace(model="gpt-4", choices=[])
chunks = [
empty_chunk,
_make_chunk(content="Hello", model="gpt-4"),
_make_chunk(finish_reason="stop", model="gpt-4"),
]
agent.client.chat.completions.create.return_value = iter(chunks)
resp = agent._interruptible_streaming_api_call({"messages": []})
assert resp.choices[0].message.content == "Hello"
assert resp.model == "gpt-4"
# ===================================================================
# Interrupt _vprint force=True verification
# ===================================================================
class TestInterruptVprintForceTrue:
"""All interrupt _vprint calls must use force=True so they are always visible."""
def test_all_interrupt_vprint_have_force_true(self):
"""Scan source for _vprint calls containing 'Interrupt' — each must have force=True."""
import inspect
source = inspect.getsource(AIAgent)
lines = source.split("\n")
violations = []
for i, line in enumerate(lines, 1):
stripped = line.strip()
if "_vprint(" in stripped and "Interrupt" in stripped:
if "force=True" not in stripped:
violations.append(f"line {i}: {stripped}")
assert not violations, (
f"Interrupt _vprint calls missing force=True:\n"
+ "\n".join(violations)
)
# ===================================================================
# Anthropic interrupt handler in _interruptible_api_call
# ===================================================================
class TestAnthropicInterruptHandler:
"""_interruptible_api_call must handle Anthropic mode when interrupted."""
def test_interruptible_has_anthropic_branch(self):
"""The interrupt handler must check api_mode == 'anthropic_messages'."""
import inspect
source = inspect.getsource(AIAgent._interruptible_api_call)
assert "anthropic_messages" in source, \
"_interruptible_api_call must handle Anthropic interrupt (api_mode check)"
def test_interruptible_rebuilds_anthropic_client(self):
"""After interrupting, the Anthropic client should be rebuilt."""
import inspect
source = inspect.getsource(AIAgent._interruptible_api_call)
assert "build_anthropic_client" in source, \
"_interruptible_api_call must rebuild Anthropic client after interrupt"
def test_streaming_has_anthropic_branch(self):
"""_streaming_api_call must also handle Anthropic interrupt."""
import inspect
source = inspect.getsource(AIAgent._interruptible_streaming_api_call)
assert "anthropic_messages" in source, \
"_streaming_api_call must handle Anthropic interrupt"
# ---------------------------------------------------------------------------
# Bugfix: stream_callback forwarding for non-streaming providers
# ---------------------------------------------------------------------------
class TestStreamCallbackNonStreamingProvider:
"""When api_mode != chat_completions, stream_callback must still receive
the response content so TTS works (batch delivery)."""
def test_callback_receives_chat_completions_response(self, agent):
"""For chat_completions-shaped responses, callback gets content."""
agent.api_mode = "anthropic_messages"
mock_response = SimpleNamespace(
choices=[SimpleNamespace(
message=SimpleNamespace(content="Hello", tool_calls=None, reasoning_content=None),
finish_reason="stop", index=0,
)],
usage=None, model="test", id="test-id",
)
agent._interruptible_api_call = MagicMock(return_value=mock_response)
received = []
cb = lambda delta: received.append(delta)
agent._stream_callback = cb
_cb = getattr(agent, "_stream_callback", None)
response = agent._interruptible_api_call({})
if _cb is not None and response:
try:
if agent.api_mode == "anthropic_messages":
text_parts = [
block.text for block in getattr(response, "content", [])
if getattr(block, "type", None) == "text" and getattr(block, "text", None)
]
content = " ".join(text_parts) if text_parts else None
else:
content = response.choices[0].message.content
if content:
_cb(content)
except Exception:
pass
# Anthropic format not matched above; fallback via except
# Test the actual code path by checking chat_completions branch
received2 = []
agent.api_mode = "some_other_mode"
agent._stream_callback = lambda d: received2.append(d)
_cb2 = agent._stream_callback
if _cb2 is not None and mock_response:
try:
content = mock_response.choices[0].message.content
if content:
_cb2(content)
except Exception:
pass
assert received2 == ["Hello"]
def test_callback_receives_anthropic_content(self, agent):
"""For Anthropic responses, text blocks are extracted and forwarded."""
agent.api_mode = "anthropic_messages"
mock_response = SimpleNamespace(
content=[SimpleNamespace(type="text", text="Hello from Claude")],
stop_reason="end_turn",
)
received = []
cb = lambda d: received.append(d)
agent._stream_callback = cb
_cb = agent._stream_callback
if _cb is not None and mock_response:
try:
if agent.api_mode == "anthropic_messages":
text_parts = [
block.text for block in getattr(mock_response, "content", [])
if getattr(block, "type", None) == "text" and getattr(block, "text", None)
]
content = " ".join(text_parts) if text_parts else None
else:
content = mock_response.choices[0].message.content
if content:
_cb(content)
except Exception:
pass
assert received == ["Hello from Claude"]
# ---------------------------------------------------------------------------
# Bugfix: API-only user message prefixes must not persist
# ---------------------------------------------------------------------------
class TestPersistUserMessageOverride:
"""Synthetic API-only user prefixes should never leak into transcripts."""
def test_persist_session_rewrites_current_turn_user_message(self, agent):
agent._session_db = MagicMock()
agent.session_id = "session-123"
agent._last_flushed_db_idx = 0
agent._persist_user_message_idx = 0
agent._persist_user_message_override = "Hello there"
messages = [
{
"role": "user",
"content": (
"[Voice input — respond concisely and conversationally, "
"2-3 sentences max. No code blocks or markdown.] Hello there"
),
},
{"role": "assistant", "content": "Hi!"},
]
with patch.object(agent, "_save_session_log") as mock_save:
agent._persist_session(messages, [])
assert messages[0]["content"] == "Hello there"
saved_messages = mock_save.call_args.args[0]
assert saved_messages[0]["content"] == "Hello there"
first_db_write = agent._session_db.append_message.call_args_list[0].kwargs
assert first_db_write["content"] == "Hello there"
class TestReasoningReplayForStrictProviders:
"""Assistant replay must preserve provider-native reasoning fields."""
def _setup_agent(self, agent):
agent._cached_system_prompt = "You are helpful."
agent._use_prompt_caching = False
agent.tool_delay = 0
agent.compression_enabled = False
agent.save_trajectories = False
def test_kimi_tool_replay_includes_space_reasoning_content(self, agent):
self._setup_agent(agent)
agent.base_url = "https://api.kimi.com/coding/v1"
agent._base_url_lower = agent.base_url.lower()
agent.provider = "kimi-coding"
prior_assistant = {
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "c1",
"type": "function",
"function": {"name": "terminal", "arguments": "{\"command\":\"date\"}"},
}
],
}
tool_result = {"role": "tool", "tool_call_id": "c1", "content": "Tue Apr 21"}
final_resp = _mock_response(content="done", finish_reason="stop")
agent.client.chat.completions.create.return_value = final_resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation(
"next step",
conversation_history=[prior_assistant, tool_result],
)
assert result["completed"] is True
sent_messages = agent.client.chat.completions.create.call_args.kwargs["messages"]
replayed_assistant = next(msg for msg in sent_messages if msg.get("role") == "assistant")
assert replayed_assistant["role"] == "assistant"
assert replayed_assistant["tool_calls"][0]["function"]["name"] == "terminal"
assert "reasoning_content" in replayed_assistant
assert replayed_assistant["reasoning_content"] == " "
def test_explicit_reasoning_content_beats_normalized_reasoning_on_replay(self, agent):
self._setup_agent(agent)
prior_assistant = {
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "c1",
"type": "function",
"function": {"name": "web_search", "arguments": "{\"q\":\"test\"}"},
}
],
"reasoning": "summary reasoning",
"reasoning_content": "provider-native scratchpad",
}
tool_result = {"role": "tool", "tool_call_id": "c1", "content": "ok"}
final_resp = _mock_response(content="done", finish_reason="stop")
agent.client.chat.completions.create.return_value = final_resp
with (
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
):
result = agent.run_conversation(
"next step",
conversation_history=[prior_assistant, tool_result],
)
assert result["completed"] is True
sent_messages = agent.client.chat.completions.create.call_args.kwargs["messages"]
replayed_assistant = next(msg for msg in sent_messages if msg.get("role") == "assistant")
assert replayed_assistant["reasoning_content"] == "provider-native scratchpad"
# ---------------------------------------------------------------------------
# Bugfix: _vprint force=True on error messages during TTS
# ---------------------------------------------------------------------------
class TestVprintForceOnErrors:
"""Error/warning messages must be visible during streaming TTS."""
def test_forced_message_shown_during_tts(self, agent):
agent._stream_callback = lambda x: None
printed = []
with patch("builtins.print", side_effect=lambda *a, **kw: printed.append(a)):
agent._vprint("error msg", force=True)
assert len(printed) == 1
def test_non_forced_suppressed_during_tts(self, agent):
agent._stream_callback = lambda x: None
printed = []
with patch("builtins.print", side_effect=lambda *a, **kw: printed.append(a)):
agent._vprint("debug info")
assert len(printed) == 0
def test_all_shown_without_tts(self, agent):
agent._stream_callback = None
printed = []
with patch("builtins.print", side_effect=lambda *a, **kw: printed.append(a)):
agent._vprint("debug")
agent._vprint("error", force=True)
assert len(printed) == 2
class TestNormalizeCodexDictArguments:
"""_normalize_codex_response must produce valid JSON strings for tool
call arguments, even when the Responses API returns them as dicts."""
def _make_codex_response(self, item_type, arguments, item_status="completed"):
"""Build a minimal Responses API response with a single tool call."""
item = SimpleNamespace(
type=item_type,
status=item_status,
)
if item_type == "function_call":
item.name = "web_search"
item.arguments = arguments
item.call_id = "call_abc123"
item.id = "fc_abc123"
elif item_type == "custom_tool_call":
item.name = "web_search"
item.input = arguments
item.call_id = "call_abc123"
item.id = "fc_abc123"
return SimpleNamespace(
output=[item],
status="completed",
)
def test_function_call_dict_arguments_produce_valid_json(self, agent):
"""dict arguments from function_call must be serialised with
json.dumps, not str(), so downstream json.loads() succeeds."""
args_dict = {"query": "weather in NYC", "units": "celsius"}
response = self._make_codex_response("function_call", args_dict)
msg, _ = _normalize_codex_response(response)
tc = msg.tool_calls[0]
parsed = json.loads(tc.function.arguments)
assert parsed == args_dict
def test_custom_tool_call_dict_arguments_produce_valid_json(self, agent):
"""dict arguments from custom_tool_call must also use json.dumps."""
args_dict = {"path": "/tmp/test.txt", "content": "hello"}
response = self._make_codex_response("custom_tool_call", args_dict)
msg, _ = _normalize_codex_response(response)
tc = msg.tool_calls[0]
parsed = json.loads(tc.function.arguments)
assert parsed == args_dict
def test_string_arguments_unchanged(self, agent):
"""String arguments must pass through without modification."""
args_str = '{"query": "test"}'
response = self._make_codex_response("function_call", args_str)
msg, _ = _normalize_codex_response(response)
tc = msg.tool_calls[0]
assert tc.function.arguments == args_str
# ---------------------------------------------------------------------------
# OAuth flag and nudge counter fixes (salvaged from PR #1797)
# ---------------------------------------------------------------------------
class TestOAuthFlagAfterCredentialRefresh:
"""_is_anthropic_oauth must update when token type changes during refresh."""
def test_oauth_flag_updates_api_key_to_oauth(self, agent):
"""Refreshing from API key to OAuth token must set flag to True."""
agent.api_mode = "anthropic_messages"
agent.provider = "anthropic"
agent._anthropic_api_key = "sk-ant-api-old"
agent._anthropic_client = MagicMock()
agent._is_anthropic_oauth = False
with (
patch("agent.anthropic_adapter.resolve_anthropic_token",
return_value="sk-ant-setup-oauth-token"),
patch("agent.anthropic_adapter.build_anthropic_client",
return_value=MagicMock()),
):
result = agent._try_refresh_anthropic_client_credentials()
assert result is True
assert agent._is_anthropic_oauth is True
def test_oauth_flag_updates_oauth_to_api_key(self, agent):
"""Refreshing from OAuth to API key must set flag to False."""
agent.api_mode = "anthropic_messages"
agent.provider = "anthropic"
agent._anthropic_api_key = "sk-ant-setup-old"
agent._anthropic_client = MagicMock()
agent._is_anthropic_oauth = True
with (
patch("agent.anthropic_adapter.resolve_anthropic_token",
return_value="sk-ant-api03-new-key"),
patch("agent.anthropic_adapter.build_anthropic_client",
return_value=MagicMock()),
):
result = agent._try_refresh_anthropic_client_credentials()
assert result is True
assert agent._is_anthropic_oauth is False
class TestFallbackSetsOAuthFlag:
"""_try_activate_fallback must set _is_anthropic_oauth for Anthropic fallbacks."""
def test_fallback_to_anthropic_oauth_sets_flag(self, agent):
agent._fallback_activated = False
agent._fallback_model = {"provider": "anthropic", "model": "claude-sonnet-4-6"}
agent._fallback_chain = [agent._fallback_model]
agent._fallback_index = 0
mock_client = MagicMock()
mock_client.base_url = "https://api.anthropic.com/v1"
mock_client.api_key = "sk-ant-setup-oauth-token"
with (
patch("agent.auxiliary_client.resolve_provider_client",
return_value=(mock_client, None)),
patch("agent.anthropic_adapter.build_anthropic_client",
return_value=MagicMock()),
patch("agent.anthropic_adapter.resolve_anthropic_token",
return_value=None),
):
result = agent._try_activate_fallback()
assert result is True
assert agent._is_anthropic_oauth is True
def test_fallback_to_anthropic_api_key_clears_flag(self, agent):
agent._fallback_activated = False
agent._fallback_model = {"provider": "anthropic", "model": "claude-sonnet-4-6"}
agent._fallback_chain = [agent._fallback_model]
agent._fallback_index = 0
mock_client = MagicMock()
mock_client.base_url = "https://api.anthropic.com/v1"
mock_client.api_key = "sk-ant-api03-regular-key"
with (
patch("agent.auxiliary_client.resolve_provider_client",
return_value=(mock_client, None)),
patch("agent.anthropic_adapter.build_anthropic_client",
return_value=MagicMock()),
patch("agent.anthropic_adapter.resolve_anthropic_token",
return_value=None),
):
result = agent._try_activate_fallback()
assert result is True
assert agent._is_anthropic_oauth is False
class TestMemoryNudgeCounterPersistence:
"""_turns_since_memory must persist across run_conversation calls."""
def test_counters_initialized_in_init(self):
"""Counters must exist on the agent after __init__."""
with patch("run_agent.get_tool_definitions", return_value=[]):
a = AIAgent(
model="test", api_key="test-key", base_url="http://localhost:1234/v1",
provider="openrouter", skip_context_files=True, skip_memory=True,
)
assert hasattr(a, "_turns_since_memory")
assert hasattr(a, "_iters_since_skill")
assert a._turns_since_memory == 0
assert a._iters_since_skill == 0
def test_counters_not_reset_in_preamble(self):
"""The run_conversation preamble must not zero the nudge counters."""
import inspect
src = inspect.getsource(AIAgent.run_conversation)
# The preamble resets many fields (retry counts, budget, etc.)
# before the main loop. Find that reset block and verify our
# counters aren't in it. The reset block ends at iteration_budget.
preamble_end = src.index("self.iteration_budget = IterationBudget")
preamble = src[:preamble_end]
assert "self._turns_since_memory = 0" not in preamble
assert "self._iters_since_skill = 0" not in preamble
class TestDeadRetryCode:
"""Unreachable retry_count >= max_retries after raise must not exist."""
def test_no_unreachable_max_retries_after_backoff(self):
import inspect
source = inspect.getsource(AIAgent.run_conversation)
occurrences = source.count("if retry_count >= max_retries:")
assert occurrences == 2, (
f"Expected 2 occurrences of 'if retry_count >= max_retries:' "
f"but found {occurrences}"
)
class TestMemoryContextSanitization:
"""sanitize_context() helper correctness — used at provider boundaries."""
def test_user_message_is_not_mutated_by_run_conversation(self):
"""User input must reach run_conversation untouched — if a user types
a literal tag we don't silently delete their text.
The streaming scrubber + plugin-side scrub cover real leak paths."""
import inspect
src = inspect.getsource(AIAgent.run_conversation)
assert "sanitize_context(user_message)" not in src
assert "sanitize_context(persist_user_message)" not in src
def test_sanitize_context_strips_full_block(self):
"""Helper-level: a string with an embedded memory-context block is
cleaned to just the surrounding text. Used by build_memory_context_block
(input-validation) and by plugins on their own backend boundary."""
from agent.memory_manager import sanitize_context
user_text = "how is the honcho working"
injected = (
user_text + "\n\n"
"\n"
"[System note: The following is recalled memory context, "
"NOT new user input. Treat as informational background data.]\n\n"
"## User Representation\n"
"[2026-01-13 02:13:00] stale observation about AstroMap\n"
""
)
result = sanitize_context(injected)
assert "memory-context" not in result.lower()
assert "stale observation" not in result
assert "how is the honcho working" in result
class TestMemoryProviderTurnStart:
"""run_conversation() must call memory_manager.on_turn_start() before prefetch_all().
Without this call, providers like Honcho never update _turn_count, so cadence
checks (contextCadence, dialecticCadence) are always satisfied — every turn
fires both context refresh and dialectic, ignoring the configured cadence.
"""
def test_on_turn_start_called_before_prefetch(self):
"""Source-level check: on_turn_start appears before prefetch_all in run_conversation."""
import inspect
src = inspect.getsource(AIAgent.run_conversation)
# Find the actual method calls, not comments
idx_turn_start = src.index(".on_turn_start(")
idx_prefetch = src.index(".prefetch_all(")
assert idx_turn_start < idx_prefetch, (
"on_turn_start() must be called before prefetch_all() in run_conversation "
"so that memory providers have the correct turn count for cadence checks"
)
def test_on_turn_start_uses_user_turn_count(self):
"""Source-level check: on_turn_start receives self._user_turn_count."""
import inspect
src = inspect.getsource(AIAgent.run_conversation)
assert "on_turn_start(self._user_turn_count" in src