style: fix import sorting (ruff I001)

Addresses reviewer suggestion to keep imports conventional.

README.md

@@ -120,26 +120,57 @@
 
 ## Table of Contents
 
-- [News](#-news)
-- [Key Features](#key-features-of-nanobot)
-- [Architecture](#️-architecture)
-- [Features](#-features)
-- [Install](#-install)
-- [Quick Start](#-quick-start)
-- [Chat Apps](#-chat-apps)
-- [Agent Social Network](#-agent-social-network)
-- [Configuration](#️-configuration)
-- [Multiple Instances](#-multiple-instances)
-- [Memory](#-memory)
-- [CLI Reference](#-cli-reference)
-- [In-Chat Commands](#-in-chat-commands)
-- [Python SDK](#-python-sdk)
-- [OpenAI-Compatible API](#-openai-compatible-api)
-- [Docker](#-docker)
-- [Linux Service](#-linux-service)
-- [Project Structure](#-project-structure)
-- [Contribute & Roadmap](#-contribute--roadmap)
-- [Star History](#-star-history)
+- [📢 News](#-news)
+- [Key Features of nanobot:](#key-features-of-nanobot)
+- [🏗️ Architecture](#️-architecture)
+- [Table of Contents](#table-of-contents)
+- [✨ Features](#-features)
+- [📦 Install](#-install)
+  - [Update to latest version](#update-to-latest-version)
+- [🚀 Quick Start](#-quick-start)
+- [💬 Chat Apps](#-chat-apps)
+- [🌐 Agent Social Network](#-agent-social-network)
+- [⚙️ Configuration](#️-configuration)
+  - [Environment Variables for Secrets](#environment-variables-for-secrets)
+  - [Providers](#providers)
+  - [Channel Settings](#channel-settings)
+    - [Retry Behavior](#retry-behavior)
+  - [Web Search](#web-search)
+    - [`tools.web.search`](#toolswebsearch)
+  - [MCP (Model Context Protocol)](#mcp-model-context-protocol)
+  - [Security](#security)
+  - [Auto Compact](#auto-compact)
+  - [Timezone](#timezone)
+  - [Unified Session](#unified-session)
+  - [Disabled Skills](#disabled-skills)
+- [🧩 Multiple Instances](#-multiple-instances)
+  - [Quick Start](#quick-start)
+  - [Path Resolution](#path-resolution)
+  - [How It Works](#how-it-works)
+  - [Minimal Setup](#minimal-setup)
+  - [Common Use Cases](#common-use-cases)
+  - [Notes](#notes)
+- [🧠 Memory](#-memory)
+- [💻 CLI Reference](#-cli-reference)
+- [💬 In-Chat Commands](#-in-chat-commands)
+- [🐍 Python SDK](#-python-sdk)
+- [🔌 OpenAI-Compatible API](#-openai-compatible-api)
+  - [Behavior](#behavior)
+  - [Endpoints](#endpoints)
+  - [curl](#curl)
+  - [File Upload (JSON base64)](#file-upload-json-base64)
+  - [File Upload (multipart/form-data)](#file-upload-multipartform-data)
+  - [Python (`requests`)](#python-requests)
+  - [Python (`openai`)](#python-openai)
+- [🐳 Docker](#-docker)
+  - [Docker Compose](#docker-compose)
+  - [Docker](#docker)
+- [🐧 Linux Service](#-linux-service)
+- [📁 Project Structure](#-project-structure)
+- [🤝 Contribute \& Roadmap](#-contribute--roadmap)
+  - [Branching Strategy](#branching-strategy)
+  - [Contributors](#contributors)
+- [⭐ Star History](#-star-history)
 
 ## ✨ Features
 

nanobot/agent/loop.py

@@ -157,6 +157,7 @@ class AgentLoop:
         channels_config: ChannelsConfig | None = None,
         timezone: str | None = None,
         session_ttl_minutes: int = 0,
+        consolidation_ratio: float = 0.5,
         hooks: list[AgentHook] | None = None,
         unified_session: bool = False,
         disabled_skills: list[str] | None = None,
@@ -236,6 +237,7 @@ class AgentLoop:
             build_messages=self.context.build_messages,
             get_tool_definitions=self.tools.get_definitions,
             max_completion_tokens=provider.generation.max_tokens,
+            consolidation_ratio=consolidation_ratio,
         )
         self.auto_compact = AutoCompact(
             sessions=self.sessions,

nanobot/agent/memory.py

@@ -361,6 +361,7 @@ class Consolidator:
         build_messages: Callable[..., list[dict[str, Any]]],
         get_tool_definitions: Callable[[], list[dict[str, Any]]],
         max_completion_tokens: int = 4096,
+        consolidation_ratio: float = 0.5,
     ):
         self.store = store
         self.provider = provider
@@ -368,6 +369,7 @@ class Consolidator:
         self.sessions = sessions
         self.context_window_tokens = context_window_tokens
         self.max_completion_tokens = max_completion_tokens
+        self.consolidation_ratio = consolidation_ratio
         self._build_messages = build_messages
         self._get_tool_definitions = get_tool_definitions
         self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = (
@@ -457,6 +459,8 @@ class Consolidator:
                 tools=None,
                 tool_choice=None,
             )
+            if response.finish_reason == "error":
+                raise RuntimeError(f"LLM returned error: {response.content}")
             summary = response.content or "[no summary]"
             self.store.append_history(summary)
             return summary
@@ -477,7 +481,7 @@ class Consolidator:
         lock = self.get_lock(session.key)
         async with lock:
             budget = self.context_window_tokens - self.max_completion_tokens - self._SAFETY_BUFFER
-            target = budget // 2
+            target = int(budget * self.consolidation_ratio)
             try:
                 estimated, source = self.estimate_session_prompt_tokens(session)
             except Exception:

nanobot/cli/commands.py

@@ -593,6 +593,7 @@ def serve(
         unified_session=runtime_config.agents.defaults.unified_session,
         disabled_skills=runtime_config.agents.defaults.disabled_skills,
         session_ttl_minutes=runtime_config.agents.defaults.session_ttl_minutes,
+        consolidation_ratio=runtime_config.agents.defaults.consolidation_ratio,
         tools_config=runtime_config.tools,
     )
 
@@ -688,6 +689,7 @@ def gateway(
         unified_session=config.agents.defaults.unified_session,
         disabled_skills=config.agents.defaults.disabled_skills,
         session_ttl_minutes=config.agents.defaults.session_ttl_minutes,
+        consolidation_ratio=config.agents.defaults.consolidation_ratio,
         tools_config=config.tools,
     )
 
@@ -967,6 +969,7 @@ def agent(
         unified_session=config.agents.defaults.unified_session,
         disabled_skills=config.agents.defaults.disabled_skills,
         session_ttl_minutes=config.agents.defaults.session_ttl_minutes,
+        consolidation_ratio=config.agents.defaults.consolidation_ratio,
         tools_config=config.tools,
     )
     restart_notice = consume_restart_notice_from_env()

nanobot/config/schema.py

@@ -89,6 +89,13 @@ class AgentDefaults(Base):
         validation_alias=AliasChoices("idleCompactAfterMinutes", "sessionTtlMinutes"),
         serialization_alias="idleCompactAfterMinutes",
     )  # Auto-compact idle threshold in minutes (0 = disabled)
+    consolidation_ratio: float = Field(
+        default=0.5,
+        ge=0.1,
+        le=0.95,
+        validation_alias=AliasChoices("consolidationRatio"),
+        serialization_alias="consolidationRatio",
+    )  # Consolidation target ratio (0.5 = 50% of budget retained after compression)
     dream: DreamConfig = Field(default_factory=DreamConfig)
 
 

nanobot/nanobot.py

@@ -84,6 +84,7 @@ class Nanobot:
             unified_session=defaults.unified_session,
             disabled_skills=defaults.disabled_skills,
             session_ttl_minutes=defaults.session_ttl_minutes,
+            consolidation_ratio=defaults.consolidation_ratio,
             tools_config=config.tools,
         )
         return cls(loop)

nanobot/providers/openai_compat_provider.py

@@ -9,11 +9,13 @@ import importlib.util
 import os
 import secrets
 import string
+import time
 import uuid
 from collections.abc import Awaitable, Callable
 from typing import TYPE_CHECKING, Any
 
 import json_repair
+from loguru import logger
 
 if os.environ.get("LANGFUSE_SECRET_KEY") and importlib.util.find_spec("langfuse"):
     from langfuse.openai import AsyncOpenAI
@@ -143,6 +145,10 @@ def _uses_openrouter_attribution(spec: "ProviderSpec | None", api_base: str | No
     return bool(api_base and "openrouter" in api_base.lower())
 
 
+_RESPONSES_FAILURE_THRESHOLD = 3
+_RESPONSES_PROBE_INTERVAL_S = 300  # 5 minutes
+
+
 def _is_direct_openai_base(api_base: str | None) -> bool:
     """Return True for direct OpenAI endpoints, not generic OpenAI-compatible gateways."""
     if not api_base:
@@ -189,6 +195,11 @@ class OpenAICompatProvider(LLMProvider):
             max_retries=0,
         )
 
+        # Responses API circuit breaker: skip after repeated failures,
+        # probe again after _RESPONSES_PROBE_INTERVAL_S seconds.
+        self._responses_failures: dict[str, int] = {}
+        self._responses_tripped_at: dict[str, float] = {}
+
     def _setup_env(self, api_key: str, api_base: str | None) -> None:
         """Set environment variables based on provider spec."""
         spec = self._spec
@@ -414,9 +425,39 @@ class OpenAICompatProvider(LLMProvider):
             return False
 
         model_name = (model or self.default_model).lower()
+        wants = False
         if reasoning_effort and reasoning_effort.lower() != "none":
-            return True
-        return any(token in model_name for token in ("gpt-5", "o1", "o3", "o4"))
+            wants = True
+        elif any(token in model_name for token in ("gpt-5", "o1", "o3", "o4")):
+            wants = True
+        if not wants:
+            return False
+
+        # Circuit breaker: skip after repeated failures, probe periodically.
+        key = f"{model_name}:{reasoning_effort or ''}"
+        failures = self._responses_failures.get(key, 0)
+        if failures >= _RESPONSES_FAILURE_THRESHOLD:
+            tripped = self._responses_tripped_at.get(key, 0.0)
+            if (time.monotonic() - tripped) < _RESPONSES_PROBE_INTERVAL_S:
+                return False
+            # Half-open: allow one probe attempt
+        return True
+
+    def _record_responses_failure(self, model: str | None, reasoning_effort: str | None) -> None:
+        key = f"{(model or self.default_model).lower()}:{reasoning_effort or ''}"
+        count = self._responses_failures.get(key, 0) + 1
+        self._responses_failures[key] = count
+        if count >= _RESPONSES_FAILURE_THRESHOLD:
+            self._responses_tripped_at[key] = time.monotonic()
+            logger.warning(
+                "Responses API circuit open for {} — falling back to Chat Completions",
+                key,
+            )
+
+    def _record_responses_success(self, model: str | None, reasoning_effort: str | None) -> None:
+        key = f"{(model or self.default_model).lower()}:{reasoning_effort or ''}"
+        self._responses_failures.pop(key, None)
+        self._responses_tripped_at.pop(key, None)
 
     @staticmethod
     def _should_fallback_from_responses_error(e: Exception) -> bool:
@@ -915,10 +956,13 @@ class OpenAICompatProvider(LLMProvider):
                         messages, tools, model, max_tokens, temperature,
                         reasoning_effort, tool_choice,
                     )
-                    return parse_response_output(await self._client.responses.create(**body))
+                    result = parse_response_output(await self._client.responses.create(**body))
+                    self._record_responses_success(model, reasoning_effort)
+                    return result
                 except Exception as responses_error:
                     if not self._should_fallback_from_responses_error(responses_error):
                         raise
+                    self._record_responses_failure(model, reasoning_effort)
 
             kwargs = self._build_kwargs(
                 messages, tools, model, max_tokens, temperature,
@@ -965,6 +1009,7 @@ class OpenAICompatProvider(LLMProvider):
                         _timed_stream(),
                         on_content_delta,
                     )
+                    self._record_responses_success(model, reasoning_effort)
                     return LLMResponse(
                         content=content or None,
                         tool_calls=tool_calls,
@@ -975,6 +1020,7 @@ class OpenAICompatProvider(LLMProvider):
                 except Exception as responses_error:
                     if not self._should_fallback_from_responses_error(responses_error):
                         raise
+                    self._record_responses_failure(model, reasoning_effort)
 
             kwargs = self._build_kwargs(
                 messages, tools, model, max_tokens, temperature,

tests/agent/test_consolidation_ratio.py

@@ -0,0 +1,179 @@
+"""Tests for the configurable consolidation_ratio feature."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from nanobot.agent.loop import AgentLoop
+import nanobot.agent.memory as memory_module
+from nanobot.bus.queue import MessageBus
+from nanobot.providers.base import GenerationSettings, LLMResponse
+
+
+def _make_loop(
+    tmp_path,
+    *,
+    estimated_tokens: int = 0,
+    context_window_tokens: int = 200,
+    consolidation_ratio: float = 0.5,
+) -> AgentLoop:
+    provider = MagicMock()
+    provider.get_default_model.return_value = "test-model"
+    provider.generation = GenerationSettings(max_tokens=0)
+    provider.estimate_prompt_tokens.return_value = (estimated_tokens, "test-counter")
+    _response = LLMResponse(content="ok", tool_calls=[])
+    provider.chat_with_retry = AsyncMock(return_value=_response)
+    provider.chat_stream_with_retry = AsyncMock(return_value=_response)
+
+    loop = AgentLoop(
+        bus=MessageBus(),
+        provider=provider,
+        workspace=tmp_path,
+        model="test-model",
+        context_window_tokens=context_window_tokens,
+        consolidation_ratio=consolidation_ratio,
+    )
+    loop.tools.get_definitions = MagicMock(return_value=[])
+    loop.consolidator._SAFETY_BUFFER = 0
+    return loop
+
+
+@pytest.mark.asyncio
+async def test_default_ratio_uses_half_budget(tmp_path, monkeypatch) -> None:
+    """With ratio=0.5 (default), target should be half of budget."""
+    loop = _make_loop(tmp_path, context_window_tokens=200, consolidation_ratio=0.5)
+    loop.consolidator.archive = AsyncMock(return_value=True)  # type: ignore[method-assign]
+
+    session = loop.sessions.get_or_create("cli:test")
+    session.messages = [
+        {"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
+        {"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
+        {"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
+        {"role": "assistant", "content": "a2", "timestamp": "2026-01-01T00:00:03"},
+        {"role": "user", "content": "u3", "timestamp": "2026-01-01T00:00:04"},
+        {"role": "assistant", "content": "a3", "timestamp": "2026-01-01T00:00:05"},
+        {"role": "user", "content": "u4", "timestamp": "2026-01-01T00:00:06"},
+    ]
+    loop.sessions.save(session)
+
+    # budget = 200 - 0 (max_tokens) - 0 (safety_buffer) = 200
+    # target = int(200 * 0.5) = 100
+    # estimated must be >= budget to trigger consolidation
+    call_count = [0]
+
+    def mock_estimate(_session):
+        call_count[0] += 1
+        if call_count[0] == 1:
+            return (250, "test")
+        return (90, "test")
+
+    loop.consolidator.estimate_session_prompt_tokens = mock_estimate  # type: ignore[method-assign]
+    monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
+
+    await loop.consolidator.maybe_consolidate_by_tokens(session)
+
+    # 250 >= 200 (budget, triggers) → 250 > 100 (target) → archive → 90 < 100, stops.
+    assert loop.consolidator.archive.await_count == 1
+
+
+@pytest.mark.asyncio
+async def test_low_ratio_aggressively_consolidates(tmp_path, monkeypatch) -> None:
+    """With ratio=0.1, target is only 10% of budget — more rounds of archiving."""
+    loop = _make_loop(tmp_path, context_window_tokens=1000, consolidation_ratio=0.1)
+    loop.consolidator.archive = AsyncMock(return_value=True)  # type: ignore[method-assign]
+
+    session = loop.sessions.get_or_create("cli:test")
+    # Interleave user/assistant so pick_consolidation_boundary can find boundaries
+    session.messages = []
+    for i in range(10):
+        session.messages.append({"role": "user", "content": f"u{i}", "timestamp": f"2026-01-01T00:00:{i:02d}"})
+        session.messages.append({"role": "assistant", "content": f"a{i}", "timestamp": f"2026-01-01T00:00:{i:02d}"})
+    loop.sessions.save(session)
+
+    # budget = 1000, target = int(1000 * 0.1) = 100
+    call_count = [0]
+
+    def mock_estimate(_session):
+        call_count[0] += 1
+        if call_count[0] == 1:
+            return (1200, "test")
+        if call_count[0] == 2:
+            return (800, "test")
+        if call_count[0] == 3:
+            return (400, "test")
+        return (50, "test")
+
+    loop.consolidator.estimate_session_prompt_tokens = mock_estimate  # type: ignore[method-assign]
+    monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
+
+    await loop.consolidator.maybe_consolidate_by_tokens(session)
+
+    # With low ratio, more rounds needed to reach target; at least 2 rounds
+    assert loop.consolidator.archive.await_count >= 2
+
+
+@pytest.mark.asyncio
+async def test_high_ratio_preserves_more_history(tmp_path, monkeypatch) -> None:
+    """With ratio=0.9, target is 90% of budget — consolidation stops sooner."""
+    loop = _make_loop(tmp_path, context_window_tokens=200, consolidation_ratio=0.9)
+    loop.consolidator.archive = AsyncMock(return_value=True)  # type: ignore[method-assign]
+
+    session = loop.sessions.get_or_create("cli:test")
+    session.messages = [
+        {"role": "user", "content": "u1", "timestamp": "2026-01-01T00:00:00"},
+        {"role": "assistant", "content": "a1", "timestamp": "2026-01-01T00:00:01"},
+        {"role": "user", "content": "u2", "timestamp": "2026-01-01T00:00:02"},
+        {"role": "assistant", "content": "a2", "timestamp": "2026-01-01T00:00:03"},
+        {"role": "user", "content": "u3", "timestamp": "2026-01-01T00:00:04"},
+        {"role": "assistant", "content": "a3", "timestamp": "2026-01-01T00:00:05"},
+        {"role": "user", "content": "u4", "timestamp": "2026-01-01T00:00:06"},
+    ]
+    loop.sessions.save(session)
+
+    # budget = 200, target = int(200 * 0.9) = 180
+    call_count = [0]
+
+    def mock_estimate(_session):
+        call_count[0] += 1
+        if call_count[0] == 1:
+            return (300, "test")
+        return (175, "test")
+
+    loop.consolidator.estimate_session_prompt_tokens = mock_estimate  # type: ignore[method-assign]
+    monkeypatch.setattr(memory_module, "estimate_message_tokens", lambda _m: 100)
+
+    await loop.consolidator.maybe_consolidate_by_tokens(session)
+
+    # 300 >= 200 (triggers) → 300 > 180 → archive → 175 < 180 → stop
+    assert loop.consolidator.archive.await_count == 1
+
+
+@pytest.mark.asyncio
+async def test_ratio_propagated_from_config_schema() -> None:
+    """Verify consolidation_ratio is parsed from config with camelCase alias."""
+    from nanobot.config.schema import AgentDefaults
+
+    # Default
+    defaults = AgentDefaults()
+    assert defaults.consolidation_ratio == 0.5
+
+    # camelCase alias
+    defaults = AgentDefaults.model_validate({"consolidationRatio": 0.3})
+    assert defaults.consolidation_ratio == 0.3
+
+    # Serialization uses alias
+    dumped = defaults.model_dump(by_alias=True)
+    assert dumped["consolidationRatio"] == 0.3
+
+
+@pytest.mark.asyncio
+async def test_ratio_validation_rejects_out_of_range() -> None:
+    """Invalid ratio values should be rejected by validation."""
+    from pydantic import ValidationError
+    from nanobot.config.schema import AgentDefaults
+
+    with pytest.raises(ValidationError):
+        AgentDefaults(consolidation_ratio=0.05)
+
+    with pytest.raises(ValidationError):
+        AgentDefaults(consolidation_ratio=1.0)

tests/agent/test_consolidator.py

@@ -65,6 +65,46 @@ class TestConsolidatorSummarize:
         assert result is None
 
 
+class TestConsolidatorArchiveErrorHandling:
+    """archive() must fall back to raw_archive when the LLM returns an error
+    response (finish_reason == 'error'), e.g. overloaded / quota exceeded.
+    See https://github.com/HKUDS/nanobot/issues/3244
+    """
+
+    async def test_archive_falls_back_on_error_finish_reason(self, consolidator, mock_provider, store):
+        """LLM returning finish_reason='error' should trigger raw_archive, not write error text."""
+        mock_provider.chat_with_retry.return_value = MagicMock(
+            content="Error: {'type': 'error', 'error': {'type': 'overloaded_error', 'message': 'overloaded_error (529)'}}",
+            finish_reason="error",
+        )
+        messages = [
+            {"role": "user", "content": "fix the auth bug"},
+            {"role": "assistant", "content": "Done, fixed the race condition."},
+        ]
+        result = await consolidator.archive(messages)
+        assert result is None
+        entries = store.read_unprocessed_history(since_cursor=0)
+        assert len(entries) == 1
+        assert "[RAW]" in entries[0]["content"]
+        assert "Error:" not in entries[0]["content"]
+
+    async def test_archive_preserves_summary_on_success(self, consolidator, mock_provider, store):
+        """Normal LLM response should still produce a proper summary entry."""
+        mock_provider.chat_with_retry.return_value = MagicMock(
+            content="User fixed a bug in the auth module.",
+            finish_reason="stop",
+        )
+        messages = [
+            {"role": "user", "content": "fix the auth bug"},
+            {"role": "assistant", "content": "Done."},
+        ]
+        result = await consolidator.archive(messages)
+        assert result == "User fixed a bug in the auth module."
+        entries = store.read_unprocessed_history(since_cursor=0)
+        assert len(entries) == 1
+        assert "[RAW]" not in entries[0]["content"]
+
+
 class TestConsolidatorTokenBudget:
     async def test_prompt_below_threshold_does_not_consolidate(self, consolidator):
         """No consolidation when tokens are within budget."""

tests/providers/test_responses_circuit_breaker.py

@@ -0,0 +1,71 @@
+"""Tests for Responses API circuit breaker in OpenAICompatProvider."""
+
+import time
+
+import pytest
+
+from nanobot.providers.openai_compat_provider import (
+    OpenAICompatProvider,
+    _RESPONSES_FAILURE_THRESHOLD,
+    _RESPONSES_PROBE_INTERVAL_S,
+)
+
+
+@pytest.fixture()
+def provider():
+    """A direct-OpenAI provider with Responses API support."""
+    p = OpenAICompatProvider.__new__(OpenAICompatProvider)
+    p.default_model = "gpt-5"
+    p._spec = type("Spec", (), {"name": "openai"})()
+    p._effective_base = "https://api.openai.com/v1"
+    p._responses_failures = {}
+    p._responses_tripped_at = {}
+    return p
+
+
+def test_responses_api_available_by_default(provider):
+    assert provider._should_use_responses_api("gpt-5", None) is True
+
+
+def test_circuit_opens_after_threshold(provider):
+    for _ in range(_RESPONSES_FAILURE_THRESHOLD):
+        provider._record_responses_failure("gpt-5", None)
+    assert provider._should_use_responses_api("gpt-5", None) is False
+
+
+def test_circuit_does_not_affect_other_models(provider):
+    for _ in range(_RESPONSES_FAILURE_THRESHOLD):
+        provider._record_responses_failure("gpt-5", None)
+    assert provider._should_use_responses_api("o4-mini", None) is True
+
+
+def test_success_resets_circuit(provider):
+    for _ in range(_RESPONSES_FAILURE_THRESHOLD):
+        provider._record_responses_failure("gpt-5", None)
+    assert provider._should_use_responses_api("gpt-5", None) is False
+    provider._record_responses_success("gpt-5", None)
+    assert provider._should_use_responses_api("gpt-5", None) is True
+
+
+def test_probe_after_interval(provider, monkeypatch):
+    for _ in range(_RESPONSES_FAILURE_THRESHOLD):
+        provider._record_responses_failure("gpt-5", None)
+    assert provider._should_use_responses_api("gpt-5", None) is False
+
+    # Fast-forward past the probe interval
+    key = "gpt-5:"
+    provider._responses_tripped_at[key] = time.monotonic() - _RESPONSES_PROBE_INTERVAL_S - 1
+    assert provider._should_use_responses_api("gpt-5", None) is True
+
+
+def test_below_threshold_still_allows(provider):
+    provider._record_responses_failure("gpt-5", None)
+    provider._record_responses_failure("gpt-5", None)
+    assert provider._should_use_responses_api("gpt-5", None) is True
+
+
+def test_reasoning_effort_keyed_separately(provider):
+    for _ in range(_RESPONSES_FAILURE_THRESHOLD):
+        provider._record_responses_failure("o3", "high")
+    assert provider._should_use_responses_api("o3", "high") is False
+    assert provider._should_use_responses_api("o3", "low") is True