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hermes-agent/tests/hermes_cli/test_model_validation.py

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"""Tests for provider-aware `/model` validation in hermes_cli.models."""
from unittest.mock import patch
from hermes_cli.models import (
copilot_model_api_mode,
fetch_github_model_catalog,
curated_models_for_provider,
fetch_api_models,
github_model_reasoning_efforts,
normalize_copilot_model_id,
normalize_provider,
parse_model_input,
probe_api_models,
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provider_label,
provider_model_ids,
validate_requested_model,
)
# -- helpers -----------------------------------------------------------------
FAKE_API_MODELS = [
"anthropic/claude-opus-4.6",
"anthropic/claude-sonnet-4.5",
"openai/gpt-5.4-pro",
"openai/gpt-5.4",
"google/gemini-3-pro-preview",
]
def _validate(model, provider="openrouter", api_models=FAKE_API_MODELS, **kw):
"""Shortcut: call validate_requested_model with mocked API."""
probe_payload = {
"models": api_models,
"probed_url": "http://localhost:11434/v1/models",
"resolved_base_url": kw.get("base_url", "") or "http://localhost:11434/v1",
"suggested_base_url": None,
"used_fallback": False,
}
with patch("hermes_cli.models.fetch_api_models", return_value=api_models), \
patch("hermes_cli.models.probe_api_models", return_value=probe_payload):
return validate_requested_model(model, provider, **kw)
# -- parse_model_input -------------------------------------------------------
class TestParseModelInput:
def test_plain_model_keeps_current_provider(self):
provider, model = parse_model_input("anthropic/claude-sonnet-4.5", "openrouter")
assert provider == "openrouter"
assert model == "anthropic/claude-sonnet-4.5"
def test_provider_colon_model_switches_provider(self):
provider, model = parse_model_input("openrouter:anthropic/claude-sonnet-4.5", "nous")
assert provider == "openrouter"
assert model == "anthropic/claude-sonnet-4.5"
def test_provider_alias_resolved(self):
provider, model = parse_model_input("glm:glm-5", "openrouter")
assert provider == "zai"
assert model == "glm-5"
def test_no_slash_no_colon_keeps_provider(self):
provider, model = parse_model_input("gpt-5.4", "openrouter")
assert provider == "openrouter"
assert model == "gpt-5.4"
def test_nous_provider_switch(self):
provider, model = parse_model_input("nous:hermes-3", "openrouter")
assert provider == "nous"
assert model == "hermes-3"
def test_empty_model_after_colon_keeps_current(self):
provider, model = parse_model_input("openrouter:", "nous")
assert provider == "nous"
assert model == "openrouter:"
def test_colon_at_start_keeps_current(self):
provider, model = parse_model_input(":something", "openrouter")
assert provider == "openrouter"
assert model == ":something"
def test_unknown_prefix_colon_not_treated_as_provider(self):
"""Colons are only provider delimiters if the left side is a known provider."""
provider, model = parse_model_input("anthropic/claude-3.5-sonnet:beta", "openrouter")
assert provider == "openrouter"
assert model == "anthropic/claude-3.5-sonnet:beta"
def test_http_url_not_treated_as_provider(self):
provider, model = parse_model_input("http://localhost:8080/model", "openrouter")
assert provider == "openrouter"
assert model == "http://localhost:8080/model"
feat(model): /model command overhaul — Phases 2, 3, 5 * feat(model): persist base_url on /model switch, auto-detect for bare /model custom Phase 2+3 of the /model command overhaul: Phase 2 — Persist base_url on model switch: - CLI: save model.base_url when switching to a non-OpenRouter endpoint; clear it when switching away from custom to prevent stale URLs leaking into the new provider's resolution - Gateway: same logic using direct YAML write Phase 3 — Better feedback and edge cases: - Bare '/model custom' now auto-detects the model from the endpoint using _auto_detect_local_model() and saves all three config values (model, provider, base_url) atomically - Shows endpoint URL in success messages when switching to/from custom providers (both CLI and gateway) - Clear error messages when no custom endpoint is configured - Updated test assertions for the additional save_config_value call Fixes #2562 (Phase 2+3) * feat(model): support custom:name:model triple syntax for named custom providers Phase 5 of the /model command overhaul. Extends parse_model_input() to handle the triple syntax: /model custom:local-server:qwen → provider='custom:local-server', model='qwen' /model custom:my-model → provider='custom', model='my-model' (unchanged) The 'custom:local-server' provider string is already supported by _get_named_custom_provider() in runtime_provider.py, which matches it against the custom_providers list in config.yaml. This just wires the parsing so users can do it from the /model slash command. Added 4 tests covering single, triple, whitespace, and empty model cases.
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def test_custom_colon_model_single(self):
"""custom:model-name → anonymous custom provider."""
provider, model = parse_model_input("custom:qwen-2.5", "openrouter")
assert provider == "custom"
assert model == "qwen-2.5"
def test_custom_triple_syntax(self):
"""custom:name:model → named custom provider."""
provider, model = parse_model_input("custom:local-server:qwen-2.5", "openrouter")
assert provider == "custom:local-server"
assert model == "qwen-2.5"
def test_custom_triple_spaces(self):
"""Triple syntax should handle whitespace."""
provider, model = parse_model_input("custom: my-server : my-model ", "openrouter")
assert provider == "custom:my-server"
assert model == "my-model"
def test_custom_triple_empty_model_falls_back(self):
"""custom:name: with no model → treated as custom:name (bare)."""
provider, model = parse_model_input("custom:name:", "openrouter")
# Empty model after second colon → no triple match, falls through
assert provider == "custom"
assert model == "name:"
# -- curated_models_for_provider ---------------------------------------------
class TestCuratedModelsForProvider:
def test_openrouter_returns_curated_list(self):
models = curated_models_for_provider("openrouter")
assert len(models) > 0
assert any("claude" in m[0] for m in models)
def test_zai_returns_glm_models(self):
models = curated_models_for_provider("zai")
assert any("glm" in m[0] for m in models)
def test_unknown_provider_returns_empty(self):
assert curated_models_for_provider("totally-unknown") == []
# -- normalize_provider ------------------------------------------------------
class TestNormalizeProvider:
def test_defaults_to_openrouter(self):
assert normalize_provider(None) == "openrouter"
assert normalize_provider("") == "openrouter"
def test_known_aliases(self):
assert normalize_provider("glm") == "zai"
assert normalize_provider("kimi") == "kimi-coding"
assert normalize_provider("moonshot") == "kimi-coding"
assert normalize_provider("github-copilot") == "copilot"
def test_case_insensitive(self):
assert normalize_provider("OpenRouter") == "openrouter"
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class TestProviderLabel:
def test_known_labels_and_auto(self):
assert provider_label("anthropic") == "Anthropic"
assert provider_label("kimi") == "Kimi / Moonshot"
assert provider_label("copilot") == "GitHub Copilot"
assert provider_label("copilot-acp") == "GitHub Copilot ACP"
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assert provider_label("auto") == "Auto"
def test_unknown_provider_preserves_original_name(self):
assert provider_label("my-custom-provider") == "my-custom-provider"
# -- provider_model_ids ------------------------------------------------------
class TestProviderModelIds:
def test_openrouter_returns_curated_list(self):
ids = provider_model_ids("openrouter")
assert len(ids) > 0
assert all("/" in mid for mid in ids)
def test_unknown_provider_returns_empty(self):
assert provider_model_ids("some-unknown-provider") == []
def test_zai_returns_glm_models(self):
assert "glm-5" in provider_model_ids("zai")
def test_copilot_prefers_live_catalog(self):
with patch("hermes_cli.auth.resolve_api_key_provider_credentials", return_value={"api_key": "gh-token"}), \
patch("hermes_cli.models._fetch_github_models", return_value=["gpt-5.4", "claude-sonnet-4.6"]):
assert provider_model_ids("copilot") == ["gpt-5.4", "claude-sonnet-4.6"]
def test_copilot_acp_reuses_copilot_catalog(self):
with patch("hermes_cli.auth.resolve_api_key_provider_credentials", return_value={"api_key": "gh-token"}), \
patch("hermes_cli.models._fetch_github_models", return_value=["gpt-5.4", "claude-sonnet-4.6"]):
assert provider_model_ids("copilot-acp") == ["gpt-5.4", "claude-sonnet-4.6"]
def test_copilot_acp_falls_back_to_copilot_defaults(self):
with patch("hermes_cli.auth.resolve_api_key_provider_credentials", side_effect=Exception("no token")), \
patch("hermes_cli.models._fetch_github_models", return_value=None):
ids = provider_model_ids("copilot-acp")
assert "gpt-5.4" in ids
assert "copilot-acp" not in ids
# -- fetch_api_models --------------------------------------------------------
class TestFetchApiModels:
def test_returns_none_when_no_base_url(self):
assert fetch_api_models("key", None) is None
def test_returns_none_on_network_error(self):
with patch("hermes_cli.models.urllib.request.urlopen", side_effect=Exception("timeout")):
assert fetch_api_models("key", "https://example.com/v1") is None
def test_probe_api_models_tries_v1_fallback(self):
class _Resp:
def __enter__(self):
return self
def __exit__(self, exc_type, exc, tb):
return False
def read(self):
return b'{"data": [{"id": "local-model"}]}'
calls = []
def _fake_urlopen(req, timeout=5.0):
calls.append(req.full_url)
if req.full_url.endswith("/v1/models"):
return _Resp()
raise Exception("404")
with patch("hermes_cli.models.urllib.request.urlopen", side_effect=_fake_urlopen):
probe = probe_api_models("key", "http://localhost:8000")
assert calls == ["http://localhost:8000/models", "http://localhost:8000/v1/models"]
assert probe["models"] == ["local-model"]
assert probe["resolved_base_url"] == "http://localhost:8000/v1"
assert probe["used_fallback"] is True
def test_probe_api_models_uses_copilot_catalog(self):
class _Resp:
def __enter__(self):
return self
def __exit__(self, exc_type, exc, tb):
return False
def read(self):
return b'{"data": [{"id": "gpt-5.4", "model_picker_enabled": true, "supported_endpoints": ["/responses"], "capabilities": {"type": "chat", "supports": {"reasoning_effort": ["low", "medium", "high"]}}}, {"id": "claude-sonnet-4.6", "model_picker_enabled": true, "supported_endpoints": ["/chat/completions"], "capabilities": {"type": "chat", "supports": {"reasoning_effort": ["low", "medium", "high"]}}}, {"id": "text-embedding-3-small", "model_picker_enabled": true, "capabilities": {"type": "embedding"}}]}'
with patch("hermes_cli.models.urllib.request.urlopen", return_value=_Resp()) as mock_urlopen:
probe = probe_api_models("gh-token", "https://api.githubcopilot.com")
assert mock_urlopen.call_args[0][0].full_url == "https://api.githubcopilot.com/models"
assert probe["models"] == ["gpt-5.4", "claude-sonnet-4.6"]
assert probe["resolved_base_url"] == "https://api.githubcopilot.com"
assert probe["used_fallback"] is False
def test_fetch_github_model_catalog_filters_non_chat_models(self):
class _Resp:
def __enter__(self):
return self
def __exit__(self, exc_type, exc, tb):
return False
def read(self):
return b'{"data": [{"id": "gpt-5.4", "model_picker_enabled": true, "supported_endpoints": ["/responses"], "capabilities": {"type": "chat", "supports": {"reasoning_effort": ["low", "medium", "high"]}}}, {"id": "text-embedding-3-small", "model_picker_enabled": true, "capabilities": {"type": "embedding"}}]}'
with patch("hermes_cli.models.urllib.request.urlopen", return_value=_Resp()):
catalog = fetch_github_model_catalog("gh-token")
assert catalog is not None
assert [item["id"] for item in catalog] == ["gpt-5.4"]
class TestGithubReasoningEfforts:
def test_gpt5_supports_minimal_to_high(self):
catalog = [{
"id": "gpt-5.4",
"capabilities": {"type": "chat", "supports": {"reasoning_effort": ["low", "medium", "high"]}},
"supported_endpoints": ["/responses"],
}]
assert github_model_reasoning_efforts("gpt-5.4", catalog=catalog) == [
"low",
"medium",
"high",
]
def test_legacy_catalog_reasoning_still_supported(self):
catalog = [{"id": "openai/o3", "capabilities": ["reasoning"]}]
assert github_model_reasoning_efforts("openai/o3", catalog=catalog) == [
"low",
"medium",
"high",
]
def test_non_reasoning_model_returns_empty(self):
catalog = [{"id": "gpt-4.1", "capabilities": {"type": "chat", "supports": {}}}]
assert github_model_reasoning_efforts("gpt-4.1", catalog=catalog) == []
class TestCopilotNormalization:
def test_normalize_old_github_models_slug(self):
catalog = [{"id": "gpt-4.1"}, {"id": "gpt-5.4"}]
assert normalize_copilot_model_id("openai/gpt-4.1-mini", catalog=catalog) == "gpt-4.1"
def test_copilot_api_mode_gpt5_uses_responses(self):
"""GPT-5+ models should use Responses API (matching opencode)."""
assert copilot_model_api_mode("gpt-5.4") == "codex_responses"
assert copilot_model_api_mode("gpt-5.4-mini") == "codex_responses"
assert copilot_model_api_mode("gpt-5.3-codex") == "codex_responses"
assert copilot_model_api_mode("gpt-5.2-codex") == "codex_responses"
assert copilot_model_api_mode("gpt-5.2") == "codex_responses"
def test_copilot_api_mode_gpt5_mini_uses_chat(self):
"""gpt-5-mini is the exception — uses Chat Completions."""
assert copilot_model_api_mode("gpt-5-mini") == "chat_completions"
def test_copilot_api_mode_non_gpt5_uses_chat(self):
"""Non-GPT-5 models use Chat Completions."""
assert copilot_model_api_mode("gpt-4.1") == "chat_completions"
assert copilot_model_api_mode("gpt-4o") == "chat_completions"
assert copilot_model_api_mode("gpt-4o-mini") == "chat_completions"
assert copilot_model_api_mode("claude-sonnet-4.6") == "chat_completions"
assert copilot_model_api_mode("claude-opus-4.6") == "chat_completions"
assert copilot_model_api_mode("gemini-2.5-pro") == "chat_completions"
def test_copilot_api_mode_with_catalog_both_endpoints(self):
"""When catalog shows both endpoints, model ID pattern wins."""
catalog = [{
"id": "gpt-5.4",
"supported_endpoints": ["/chat/completions", "/responses"],
}]
# GPT-5.4 should use responses even though chat/completions is listed
assert copilot_model_api_mode("gpt-5.4", catalog=catalog) == "codex_responses"
def test_copilot_api_mode_with_catalog_only_responses(self):
catalog = [{
"id": "gpt-5.4",
"supported_endpoints": ["/responses"],
"capabilities": {"type": "chat"},
}]
assert copilot_model_api_mode("gpt-5.4", catalog=catalog) == "codex_responses"
# -- validate — format checks -----------------------------------------------
class TestValidateFormatChecks:
def test_empty_model_rejected(self):
result = _validate("")
assert result["accepted"] is False
assert "empty" in result["message"]
def test_whitespace_only_rejected(self):
result = _validate(" ")
assert result["accepted"] is False
def test_model_with_spaces_rejected(self):
result = _validate("anthropic/ claude-opus")
assert result["accepted"] is False
def test_no_slash_model_still_probes_api(self):
result = _validate("gpt-5.4", api_models=["gpt-5.4", "gpt-5.4-pro"])
assert result["accepted"] is True
assert result["persist"] is True
def test_no_slash_model_rejected_if_not_in_api(self):
result = _validate("gpt-5.4", api_models=["openai/gpt-5.4"])
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
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assert result["accepted"] is True
assert "not found" in result["message"]
# -- validate — API found ----------------------------------------------------
class TestValidateApiFound:
def test_model_found_in_api(self):
result = _validate("anthropic/claude-opus-4.6")
assert result["accepted"] is True
assert result["persist"] is True
assert result["recognized"] is True
def test_model_found_for_custom_endpoint(self):
result = _validate(
"my-model", provider="openrouter",
api_models=["my-model"], base_url="http://localhost:11434/v1",
)
assert result["accepted"] is True
assert result["persist"] is True
assert result["recognized"] is True
# -- validate — API not found ------------------------------------------------
class TestValidateApiNotFound:
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
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def test_model_not_in_api_accepted_with_warning(self):
result = _validate("anthropic/claude-nonexistent")
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
assert result["accepted"] is True
assert result["persist"] is True
assert "not found" in result["message"]
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
def test_warning_includes_suggestions(self):
result = _validate("anthropic/claude-opus-4.5")
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
assert result["accepted"] is True
assert "Similar models" in result["message"]
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
# -- validate — API unreachable — accept and persist everything ----------------
class TestValidateApiFallback:
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
def test_any_model_accepted_when_api_down(self):
result = _validate("anthropic/claude-opus-4.6", api_models=None)
assert result["accepted"] is True
assert result["persist"] is True
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
def test_unknown_model_also_accepted_when_api_down(self):
"""No hardcoded catalog gatekeeping — accept, persist, and warn."""
result = _validate("anthropic/claude-next-gen", api_models=None)
assert result["accepted"] is True
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
assert result["persist"] is True
assert "could not reach" in result["message"].lower()
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
def test_zai_model_accepted_when_api_down(self):
result = _validate("glm-5", provider="zai", api_models=None)
assert result["accepted"] is True
assert result["persist"] is True
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
def test_unknown_provider_accepted_when_api_down(self):
result = _validate("some-model", provider="totally-unknown", api_models=None)
assert result["accepted"] is True
fix: stop rejecting unlisted models, accept with warning instead * fix: use session_key instead of chat_id for adapter interrupt lookups monitor_for_interrupt() in _run_agent was using source.chat_id to query the adapter's has_pending_interrupt() and get_pending_message() methods. But the adapter stores interrupt events under build_session_key(source), which produces a different string (e.g. 'agent:main:telegram:dm' vs '123456'). This key mismatch meant the interrupt was never detected through the adapter path, which is the only active interrupt path for all adapter-based platforms (Telegram, Discord, Slack, etc.). The gateway-level interrupt path (in dispatch_message) is unreachable because the adapter intercepts the 2nd message in handle_message() before it reaches dispatch_message(). Result: sending a new message while subagents were running had no effect — the interrupt was silently lost. Fix: replace all source.chat_id references in the interrupt-related code within _run_agent() with the session_key parameter, which matches the adapter's storage keys. Also adds regression tests verifying session_key vs chat_id consistency. * debug: add file-based logging to CLI interrupt path Temporary instrumentation to diagnose why message-based interrupts don't seem to work during subagent execution. Logs to ~/.hermes/interrupt_debug.log (immune to redirect_stdout). Two log points: 1. When Enter handler puts message into _interrupt_queue 2. When chat() reads it and calls agent.interrupt() This will reveal whether the message reaches the queue and whether the interrupt is actually fired. * fix: accept unlisted models with warning instead of rejecting validate_requested_model() previously hard-rejected any model not found in the provider's API listing. This was too aggressive — users on higher plan tiers (e.g. Z.AI Pro/Max) may have access to models not shown in the public listing (like glm-5 on coding endpoints). Changes: - validate_requested_model: accept unlisted models with a warning note instead of blocking. The model is saved to config and used immediately. - Z.AI setup: always offer glm-5 in the model list regardless of whether a coding endpoint was detected. Pro/Max plans support it. - Z.AI setup detection message: softened from 'GLM-5 is not available' to 'GLM-5 may still be available depending on your plan tier'
2026-03-12 16:02:35 -07:00
assert result["persist"] is True
def test_custom_endpoint_warns_with_probed_url_and_v1_hint(self):
with patch(
"hermes_cli.models.probe_api_models",
return_value={
"models": None,
"probed_url": "http://localhost:8000/v1/models",
"resolved_base_url": "http://localhost:8000",
"suggested_base_url": "http://localhost:8000/v1",
"used_fallback": False,
},
):
result = validate_requested_model(
"qwen3",
"custom",
api_key="local-key",
base_url="http://localhost:8000",
)
assert result["accepted"] is True
assert result["persist"] is True
assert "http://localhost:8000/v1/models" in result["message"]
assert "http://localhost:8000/v1" in result["message"]