fix: 增强 LLM 参数适配层 (#1317)

* fix: add llm generation parameter adaptation * fix(review-feedback-1317): 补充官方来源链接或明确写成基于当前兼容测试的保守策略，并说明验证环境 * fix(review-feedback-1317): 补充官方文档/公告链接、当前依赖/运行时验证依据，以及 provider override 的预期扩展方式 * feat: 增强 LLM 参数错误自愈 (#1318) * fix: add llm generation parameter adaptation * fix: add llm parameter error recovery * fix(review-feedback-1318): Scope recovery cache to the endpoint and making recovery caching * fix(review-feedback-1318): Avoid forcing 1.0 for all default-only temperature errors * fix(review-feedback-1318): 基于目标分支解决冲突，并确保解决后 diff 仍只包含本 PR 声称的 LLM 参数自愈增量 * fix(review-feedback-1318): Scope legacy Router recoveries to their endpoint * fix(review-feedback-1318): 解决冲突并基于解决后的最终 diff 重新确认测试结果 * fix(review-feedback-1318): 基于目标 base 解决冲突，再重新确认 diff 与 CI * fix(review-feedback-1318): 解决冲突后重新确认 diff 与 CI * fix(review-feedback-1318): 补充覆盖 Analyzer legacy multi-key Router 的回归测试
2026-09-20 02:43:35 +08:00 · 2026-05-16 21:31:28 +08:00
parent c169920b0b
commit 4aad40cdd4
16 changed files with 1554 additions and 195 deletions
--- a/.env.example
+++ b/.env.example
@@ -96,14 +96,18 @@ GEMINI_API_KEY=
 # LLM_OLLAMA_MODELS=qwen3:8b

 # 采样温度（0.0-2.0，默认 0.7；0 确定性最高，2 随机性最高）
-# Kimi K2.6 兼容来源：
+# 严格 temperature 兼容来源：
 # - Moonshot API / 模型文档：https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart
 # - Moonshot 官方模型卡（评测默认 temperature = 1.0）：https://huggingface.co/moonshotai/Kimi-K2.6
+# - OpenAI Chat Completions 规范：https://platform.openai.com/docs/api-reference/chat/create
 # - LiteLLM OpenAI-Compatible 规范：https://docs.litellm.ai/docs/providers/openai_compatible
 # 当前仓库运行时依赖约束：litellm>=1.80.10,!=1.82.7,!=1.82.8,<2.0.0（显式排除 PyPI 事故版本，见 requirements.txt）。
-# 因此 kimi-k2.6 会自动改用 1.0，避免 API 拒绝请求；其他模型和 fallback 仍使用你配置的 LLM_TEMPERATURE。
-# Web 设置页 / 桌面端导入不会静默清空或改写 LLM_TEMPERATURE；只在真正发请求前按 Kimi 要求临时归一化。
-# 如果主模型切回非 Kimi，原本配置的温度会自动恢复；最小回滚方式是回退本次 Kimi 固定温度改动。
+# 因此 kimi-k2.6 会自动改用 1.0/0.6；GPT-5 / o 系列等默认温度模型会省略 temperature，避免 API 拒绝请求。
+# 若兼容平台返回明确的参数不支持错误，运行时会在当前请求内修正参数并重试一次；成功策略只做进程内缓存。
+# top_p、presence_penalty、frequency_penalty、seed 若返回“不支持参数”，同样会触发本次请求级别的修正与重试（不改写 LLM_TEMPERATURE）。
+# 其他模型和 fallback 仍使用你配置的 LLM_TEMPERATURE。
+# Web 设置页 / 桌面端导入不会静默清空或改写 LLM_TEMPERATURE；只在真正发请求前临时适配模型参数。
+# 如果主模型切回普通模型，原本配置的温度会自动恢复；最小回滚方式是回退本次 LLM 参数适配改动。
 # LLM_TEMPERATURE=0.7

 # --- 多渠道配置（可选，也可在 Web 设置页配置）---
--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -11,6 +11,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/).

 <!-- 新条目格式：- [类型] 描述（类型取值：新功能/改进/修复/文档/测试/chore）-->
 <!-- 每条独立一行追加到本段末尾，无需分类标题，合并时冲突最小 -->
+- [修复] 抽出 LiteLLM 生成参数适配层，对严格 temperature 模型按请求临时固定或省略参数，避免 GPT-5 / o 系列与 Kimi K2.6 拒绝默认温度请求。
+- [改进] LiteLLM 参数错误支持一次请求内自动修正重试，并在成功后进程内缓存策略，降低新模型参数兼容问题的人工配置成本。
+- [文档] 补充 Issue #1316 参数自愈改动的外部兼容依据、运行时配置清理边界与回滚证据；并在 `tests/test_system_config_service.py` 增加清理路径下 `LLM_TEMPERATURE` 保持不变的回归用例。
+- [文档] 补充严格 temperature 兼容语义的官方来源、运行时依赖约束与 `LLM_TEMPERATURE` 回退/不回写路径说明。

 - [修复] 统一 Windows 桌面安装包与自动更新元数据文件名，避免 Release 中出现重复安装包并阻断 `latest.yml` 指向不存在附件。
 - [修复] 桌面端启动 WebUI 时为入口页增加 no-cache 响应头和版本化 cache-busting URL，避免安装新版后 Electron 继续复用旧 WebUI 缓存。
--- a/docs/LLM_CONFIG_GUIDE.md
+++ b/docs/LLM_CONFIG_GUIDE.md
@@ -199,17 +199,20 @@ LITELLM_MODEL=ollama/qwen3:8b
 - 如果当前环境没有任何有效 Agent 模型链路，问股页面会继续按失败语义返回，并直接展示后端真实配置诊断；补齐任一有效模型来源后即可恢复，无需额外执行配置迁移脚本。
 - 推荐的新配置方式仍然是显式设置 `LITELLM_MODEL` / `AGENT_LITELLM_MODEL` 或使用 `LLM_CHANNELS`；legacy provider keys 目前保留为兼容回退路径，方便旧 `.env`、本地 macOS 开发环境和历史部署平滑继续运行。

-### Kimi K2.6 固定 temperature 兼容说明
+### 严格 temperature 模型兼容说明

 - Moonshot 官方说明 Kimi API 兼容 OpenAI 接口，Base URL 使用 `https://api.moonshot.ai/v1`：<https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart>
 - LiteLLM 官方要求 OpenAI Compatible 渠道模型名使用 `openai/` 前缀：<https://docs.litellm.ai/docs/providers/openai_compatible>
 - Moonshot 官方兼容性文档区分两种固定值：**thinking 模式固定 `1.0`，non-thinking 模式固定 `0.6`**；传其它值会被接口拒绝：<https://platform.moonshot.ai/docs/guide/compatibility#parameters-differences-in-request-body>
+- OpenAI Chat Completions 规范中 `temperature` 是可选参数；对 GPT-5 / o 系列等只接受默认温度的模型，本项目会在请求层省略 `temperature`，让服务端使用默认值，而不是改写你的 `LLM_TEMPERATURE`：<https://platform.openai.com/docs/api-reference/chat/create>
 - 当前仓库的运行时依赖约束是 `litellm>=1.80.10,!=1.82.7,!=1.82.8,<2.0.0`（见 `requirements.txt`）；本次兼容逻辑按该约束回归验证了主分析、大盘复盘、Agent 直连 LiteLLM，以及系统设置页的渠道连通性测试。
 - 因此本项目会在请求发出前按**实际请求模式**归一化 `kimi-k2.6` 及其 `kimi-k2.6-*` 变体：默认 / thinking 路径使用 `temperature=1.0`；如果你的 LiteLLM YAML 路由别名里显式写了 `litellm_params.extra_body.thinking.type: disabled`（或等价 non-thinking 配置），则自动切到 `temperature=0.6`。你在 `.env` 或 Web 设置里保存的 `LLM_TEMPERATURE` 不会被改写。
- `SystemConfigService` 在 Web 设置保存 / 桌面端 `.env` 导入时只更新你提交的 key，不会因为切到 Kimi 静默清空、迁移或重写已有 `LLM_TEMPERATURE`；渠道测试请求里临时使用的 `1.0/0.6` 也不会回写到配置文件。
- 非 Kimi 主模型、非 Kimi fallback 以及切回普通模型后的请求，仍继续使用你配置的温度；也就是说旧配置无需迁移，切换模型即可自动恢复原行为。
+- 如果兼容平台对未收录的新模型返回明确的参数错误（例如 `temperature` 不支持、只能使用默认 `1.0`、`top_p` 不支持），运行时会对**当前请求**做一次参数修正并重试；只有重试成功后才把该策略缓存在当前进程内。该缓存不会写回 `.env`，服务重启后会重新按配置与适配规则判断。
+- 对已经产生部分内容的流式响应，系统不会在半截输出后切换参数；仍沿用原有“同模型非流式重试 / fallback 模型”的稳定路径，避免拼接出不一致的回答。
+- `SystemConfigService` 在 Web 设置保存 / 桌面端 `.env` 导入时只更新你提交的 key，不会因为切到严格 temperature 模型静默清空、迁移或重写已有 `LLM_TEMPERATURE`；渠道测试请求里的临时参数策略也不会回写到配置文件。
+- 非严格主模型、非严格 fallback 以及切回普通模型后的请求，仍继续使用你配置的温度；也就是说旧配置无需迁移，切换模型即可自动恢复原行为。
 - 本仓库兼容性回归覆盖见：`tests/test_llm_channel_config.py`、`tests/test_market_analyzer_generate_text.py`、`tests/test_agent_pipeline.py`、`tests/test_system_config_service.py`。
- 最小回滚方式：直接回退本次 Kimi 固定温度相关改动，无需单独迁移已有 `LLM_TEMPERATURE` 配置。
+- 最小回滚方式：直接回退本次 LLM 参数适配相关改动，无需单独迁移已有 `LLM_TEMPERATURE` 配置。

 ### 兼容性与回退复核清单（按 PR 审核口径）

@@ -224,6 +227,12 @@ LITELLM_MODEL=ollama/qwen3:8b
 > **致命避坑说明**：如果你启用了 `LLM_CHANNELS`，那么你直接写在外面的 `DEEPSEEK_API_KEY` 或 `OPENAI_API_KEY` 将**全部失效（系统一律无视）**！二者**选其一即可**，千万不要既写了新手模式又写了渠道模式结果产生冲突。
 > **Docker 注意**：如果你在 `docker compose environment:` 或 `docker run -e` 中显式传入 `LITELLM_MODEL`、`LLM_CHANNELS`、`LLM_DEEPSEEK_MODELS` 等变量，容器重启后这些环境变量会覆盖 Web 设置页写入的 `.env`，需要同步修改部署配置。

+### 兼容依据与回退审计说明（本次 PR 适配说明）
+
+- 官方与运行时兼容依据采用两层：第一层为官方接口语义（LiteLLM OpenAI-compatible 路由、OpenAI Chat Completions、Moonshot/Kimi 文档与官方模型说明）；第二层为本仓库当前运行时语义（`litellm>=1.80.10,!=1.82.7,!=1.82.8,<2.0.0`）下的实际错误归类。
+- 本次兼容恢复只使用“本地运行时错误归类 + 单请求修正重试 + 进程内缓存”策略，不写入 `.env`、不做配置迁移，仅在执行路径上动态规避不支持参数（`temperature`、`top_p`、`presence_penalty`、`frequency_penalty`、`seed`）。若要回退，不需要额外迁移命令，恢复旧值即可。
+- 回归与证据：`tests/test_llm_param_recovery.py`、`tests/test_system_config_service.py`、`tests/test_llm_channel_config.py`、`tests/test_system_config_api.py`、`tests/test_market_analyzer_generate_text.py`、`tests/test_agent_pipeline.py`；桌面导入与运行时清理回退另有 `test_import_desktop_env_restores_runtime_models_after_cleanup` 直接覆盖。
+
 ---

 ## 方式三：YAML 高级配置（适合老手自定义）
@@ -318,6 +327,7 @@ VISION_PROVIDER_PRIORITY=gemini,anthropic,openai
 | **我写了好几家的Key，为什么死活只有一个生效？修改还没用？** | 你把 **极简模式** 和 **渠道模式** 混着写了！ | 想好一条路走到黑——只要简单就删掉 `LLM_CHANNELS` 开头的；想要丰富备用切换就要全部转投到 `LLM_CHANNELS` 下的编制里。 |
 | **错误码报 400 或 401 或 Invalid API Key** | API Key 填错、少复制了一截、账号充值没到账、或者模型名字敲错（极度常见）。 | 1. 检查复制的 Key 前后是否有误填空格。<br> 2. 检查 Base URL 最后是不是少了一个 `/v1`。<br> 3. 检查模型名是否少写了 `openai/` 之类的前缀！ |
 | **Kimi K2.6 报 `invalid temperature`（可能提示只允许 `1.0` 或 `0.6`）** | 该模型按 thinking / non-thinking 模式要求不同固定 temperature；旧配置或调用入口可能还在传 `0.7`。 | 升级后系统会对 `kimi-k2.6` 默认 / thinking 请求自动使用 `temperature=1.0`；如果你在 LiteLLM YAML 路由里显式关闭 thinking，则自动改用 `0.6`。模型名建议写成 `openai/kimi-k2.6` 并配合 Moonshot / 聚合平台的 OpenAI 兼容 Base URL 与 API Key。非 Kimi fallback 仍会继续使用你配置的 `LLM_TEMPERATURE`。 |
+| **GPT-5 / o 系列报 `temperature` 不支持或只允许默认值** | 这类模型只接受服务端默认采样参数，但旧调用入口会显式传 `0.7`。 | 升级后请求层会省略 `temperature`，让服务端使用默认值；`.env` / Web 设置中的 `LLM_TEMPERATURE` 不会被改写，切回普通模型后仍按原值发送。 |
 | **转圈转不停，最后报 Timeout / ConnectionRefused 等** | 1. 在国内使用国外原版（像 Google、OpenAI），没开代理被墙了。<br>2. 你买的云服务器压根不能出境。 | 非常推荐使用**国内官方**（如DeepSeek、阿里）或者各种**兼容 OpenAI 的聚合中转接口**。因为中转站把网络问题帮你解决好了。 |
 | **Ollama 报 404、`Could not get model info` 或 `api/generate/api/show`** | 误用 `OPENAI_BASE_URL` 配置 Ollama，系统会错误拼接 URL | 改用 `OLLAMA_API_BASE=http://localhost:11434` 或渠道模式（`LLM_CHANNELS=ollama` + `LLM_OLLAMA_BASE_URL`） |

--- a/docs/LLM_CONFIG_GUIDE_EN.md
+++ b/docs/LLM_CONFIG_GUIDE_EN.md
@@ -192,21 +192,30 @@ LITELLM_MODEL=ollama/qwen3:8b
 - If the current environment has no valid Agent model path at all, the ask-stock page still returns a failure and now surfaces the backend's real configuration diagnosis. As soon as you restore any valid model source, the flow recovers without running any migration step.
 - The recommended forward path is still to configure `LITELLM_MODEL` / `AGENT_LITELLM_MODEL` explicitly or move to `LLM_CHANNELS`; legacy provider keys remain a compatibility fallback for older `.env` files, local macOS development, and existing deployments.

-### Kimi K2.6 Fixed-Temperature Compatibility Notes
+### Strict Temperature Model Compatibility Notes

 - Moonshot officially documents Kimi as an OpenAI-compatible API, with `https://api.moonshot.ai/v1` as the base URL: <https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart>
 - LiteLLM officially requires the `openai/` prefix for OpenAI-compatible model routing: <https://docs.litellm.ai/docs/providers/openai_compatible>
 - Moonshot's compatibility docs distinguish two fixed values: **thinking mode must use `1.0`, while non-thinking mode must use `0.6`**; other values are rejected by the API: <https://platform.moonshot.ai/docs/guide/compatibility#parameters-differences-in-request-body>
+- The OpenAI Chat Completions API treats `temperature` as optional. For GPT-5 / o-series style models that only accept the provider default temperature, this project omits `temperature` at request time instead of rewriting your saved `LLM_TEMPERATURE`: <https://platform.openai.com/docs/api-reference/chat/create>
 - The current runtime dependency constraint in this repository is `litellm>=1.80.10,!=1.82.7,!=1.82.8,<2.0.0` (see `requirements.txt`); this compatibility fix is regression-covered under that constraint across the main analyzer, market review, direct Agent LiteLLM calls, and the system-settings channel connectivity test path.
 - This repository therefore normalizes `kimi-k2.6` and `kimi-k2.6-*` right before dispatch based on the **actual request mode**: default / thinking requests use `temperature=1.0`; if your LiteLLM YAML route alias explicitly sets `litellm_params.extra_body.thinking.type: disabled` (or an equivalent non-thinking override), it automatically switches to `temperature=0.6`. Your saved `LLM_TEMPERATURE` value in `.env` or the Web settings is not rewritten.
- `SystemConfigService` only updates keys that you actually submit when saving from the Web settings page or importing a desktop `.env`; switching to Kimi does not silently clear, migrate, or rewrite an existing `LLM_TEMPERATURE`. The temporary `1.0/0.6` used for Kimi channel tests is request-scoped and is not persisted back into the config file.
- Non-Kimi primary models, non-Kimi fallbacks, and any request after switching away from Kimi still use your configured temperature. Existing configs do not need migration; changing the model restores the original behavior automatically.
+- If a compatible platform returns an explicit parameter error for a not-yet-profiled model, such as unsupported `temperature`, default-only `1.0`, or unsupported `top_p`, the runtime repairs the **current request** and retries once. The strategy is cached only in the current process after the retry succeeds; it is never written back to `.env`, and a service restart re-evaluates the configured rules normally.
+- For streaming responses that already produced partial content, the runtime does not switch parameters mid-output. It keeps the existing same-model non-stream retry / fallback-model path to avoid stitching inconsistent answers together.
+- `SystemConfigService` only updates keys that you actually submit when saving from the Web settings page or importing a desktop `.env`; switching to a strict-temperature model does not silently clear, migrate, or rewrite an existing `LLM_TEMPERATURE`. Temporary request-time parameter strategies are not persisted back into the config file.
+- Non-strict primary models, non-strict fallbacks, and any request after switching back to a regular model still use your configured temperature. Existing configs do not need migration; changing the model restores the original behavior automatically.
 - Repository-side compatibility coverage lives in `tests/test_llm_channel_config.py`, `tests/test_market_analyzer_generate_text.py`, `tests/test_agent_pipeline.py`, and `tests/test_system_config_service.py`.
- Minimal rollback: revert only the Kimi fixed-temperature change set; no separate `LLM_TEMPERATURE` migration is required.
+- Minimal rollback: revert only the LLM generation-parameter adaptation change set; no separate `LLM_TEMPERATURE` migration is required.

 > **Critical Warning**: If you enable `LLM_CHANNELS`, any standard `DEEPSEEK_API_KEY` or `OPENAI_API_KEY` declared independently will be **completely ignored**. **Use only one mode** to prevent configuration conflicts.
 > **Docker note**: If `LITELLM_MODEL`, `LLM_CHANNELS`, `LLM_DEEPSEEK_MODELS`, or related variables are explicitly passed through `docker compose environment:` or `docker run -e`, they will override the `.env` written by the Web settings page after a container restart. Update the deployment environment at the same time.

+### Compatibility evidence and rollback audit notes (for this recovery change)
+
+- Compatibility is validated in two layers: first-party provider/API contract references (LiteLLM OpenAI-compatible routing, OpenAI Chat Completions, Moonshot/Kimi docs and model notes), and second the current runtime implementation in this repository under `litellm>=1.80.10,!=1.82.7,!=1.82.8,<2.0.0`.
+- This recovery path is runtime-only and intentionally local: exception classification + one in-request repair retry + in-process cache. It does not rewrite `.env`, migrate saved config keys, or alter legacy values; it only omits/adjusts request parameters (`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `seed`) for the current call. Rolling back requires no migration; restore previous settings and model/provider selection.
+- Regression evidence for this path is in `tests/test_llm_param_recovery.py`, `tests/test_system_config_service.py`, `tests/test_llm_channel_config.py`, `tests/test_system_config_api.py`, `tests/test_market_analyzer_generate_text.py`, `tests/test_agent_pipeline.py`; desktop backup import restore is directly covered by `test_import_desktop_env_restores_runtime_models_after_cleanup`.
+
 ---

 ## Method 3: Advanced YAML Config (Expert Setup)
@@ -285,6 +294,7 @@ Afraid you got the config wrong? Type the following commands in your terminal to
 | **I added multiple provider Keys, why is only one working?** | You mixed the **Simple Mode** and **Channels Mode**! | Choose one path. For simple setups, delete anything starting with `LLM_CHANNELS`. To use multi-model fallbacks, migrate all your Keys into the `LLM_CHANNELS` setup. |
 | **Returns 400, 401, or Invalid API Key** | The API Key is wrong, copied incompletely, account lacks credits, or you mistyped the model name (extremely common). | 1. Ensure there are no spaces at the start/end of your Key.<br> 2. Ensure your Base URL ends with `/v1`.<br> 3. Check if you forgot the `openai/` prefix on the model name! |
 | **Kimi K2.6 returns `invalid temperature` (it may say only `1.0` or `0.6` is allowed)** | The model requires different fixed temperatures for thinking vs non-thinking mode, while older config or call paths may still pass `0.7`. | After this fix, default / thinking `kimi-k2.6` requests automatically use `temperature=1.0`; if you explicitly disable thinking in a LiteLLM YAML route, the request automatically uses `0.6` instead. Prefer `openai/kimi-k2.6` with your Moonshot or relay OpenAI-compatible Base URL and API key. Non-Kimi fallbacks still keep your configured `LLM_TEMPERATURE`. |
+| **GPT-5 / o-series returns that `temperature` is unsupported or only the default is allowed** | These models only accept the provider default sampling parameters, while older call paths may still send `0.7`. | The request layer now omits `temperature` so the provider default is used. Your `.env` / Web `LLM_TEMPERATURE` is not rewritten, and regular models keep using it after you switch back. |
 | **Spins endlessly, eventually hits Timeout/ConnectionRefused** | You are using restricted APIs (like Google/OpenAI) in a blocked region without a proxy, or your cloud server lacks external internet access. | Highly recommend using **official regional APIs** (like DeepSeek) or **OpenAI-compatible relay platforms**. Third-party platforms bypass these network constraints. |
 | **Ollama returns 404, `Could not get model info`, or `api/generate/api/show`** | Using `OPENAI_BASE_URL` for Ollama makes the system concatenate URLs incorrectly | Use `OLLAMA_API_BASE=http://localhost:11434` or channel mode (`LLM_CHANNELS=ollama` + `LLM_OLLAMA_BASE_URL`) instead |

--- a/src/agent/llm_adapter.py
+++ b/src/agent/llm_adapter.py
@@ -23,8 +23,9 @@ from src.config import (
    get_configured_llm_models,
    get_effective_agent_models_to_try,
    get_effective_agent_primary_model,
-    normalize_litellm_temperature,
 )
+from src.llm.errors import call_litellm_with_param_recovery
+from src.llm.generation_params import apply_litellm_generation_params

 logger = logging.getLogger(__name__)

@@ -156,6 +157,7 @@ class LLMToolAdapter:
        config = config or get_config()
        self._config = config
        self._router = None          # litellm Router (multi-key primary model)
+        self._legacy_router_model_list: List[Dict[str, Any]] = []
        self._litellm_available = False
        self._register_custom_model_pricing()
        self._init_litellm()
@@ -186,6 +188,7 @@ class LLMToolAdapter:
    def _init_litellm(self) -> None:
        """Initialize litellm Router from channels / YAML / legacy keys."""
        config = self._config
+        self._legacy_router_model_list = []
        litellm_model = get_effective_agent_primary_model(config)
        if not litellm_model:
            logger.warning("Agent LLM: no effective primary model configured")
@@ -232,6 +235,7 @@ class LLMToolAdapter:
                }
                for k in keys
            ]
+            self._legacy_router_model_list = legacy_model_list
            self._router = Router(
                model_list=legacy_model_list,
                routing_strategy="simple-shuffle",
@@ -404,12 +408,6 @@ class LLMToolAdapter:
        call_kwargs: Dict[str, Any] = {
            "model": model,
            "messages": openai_messages,
-            "temperature": normalize_litellm_temperature(
-                model,
-                self._get_temperature() if temperature is None else temperature,
-                model_list=self._config.llm_model_list,
-                request_overrides={"extra_body": extra} if extra else None,
-            ),
        }
        if max_tokens is not None:
            call_kwargs["max_tokens"] = max_tokens
@@ -426,21 +424,53 @@ class LLMToolAdapter:
        use_channel_router = self._has_channel_config()
        _router_model_names = set(get_configured_llm_models(self._config.llm_model_list))
        agent_primary_model = get_effective_agent_primary_model(self._config)
-        if use_channel_router and self._router and model in _router_model_names:
-            # Channel / YAML path: Router manages all models in its model_list
-            response = self._router.completion(**call_kwargs)
-        elif self._router and model == agent_primary_model and not use_channel_router:
-            # Legacy path: Router for primary model multi-key
-            response = self._router.completion(**call_kwargs)
-        else:
-            # Legacy/direct-env path: direct call (also handles direct-env
-            # providers like groq/ or bedrock/ that are not in the Router
-            # model_list even when channel mode is active)
+        uses_router = (
+            bool(use_channel_router and self._router and model in _router_model_names)
+            or bool(self._router and model == agent_primary_model and not use_channel_router)
+        )
+        recovery_model_list = self._config.llm_model_list
+        if self._router and model == agent_primary_model and not use_channel_router:
+            recovery_model_list = self._legacy_router_model_list or self._config.llm_model_list
+        if not uses_router:
            keys = get_api_keys_for_model(model, self._config)
            if keys:
                call_kwargs["api_key"] = keys[0]
            call_kwargs.update(extra_litellm_params(model, self._config))
-            response = litellm.completion(**call_kwargs)
+        call_kwargs = apply_litellm_generation_params(
+            call_kwargs,
+            model,
+            self._get_temperature() if temperature is None else temperature,
+            model_list=recovery_model_list,
+        )
+        if use_channel_router and self._router and model in _router_model_names:
+            # Channel / YAML path: Router manages all models in its model_list
+            response = call_litellm_with_param_recovery(
+                lambda kwargs: self._router.completion(**kwargs),
+                model=model,
+                call_kwargs=call_kwargs,
+                model_list=recovery_model_list,
+                logger=logger,
+            )
+        elif self._router and model == agent_primary_model and not use_channel_router:
+            # Legacy path: Router for primary model multi-key
+            response = call_litellm_with_param_recovery(
+                lambda kwargs: self._router.completion(**kwargs),
+                model=model,
+                call_kwargs=call_kwargs,
+                model_list=recovery_model_list,
+                logger=logger,
+            )
+        else:
+            # Legacy/direct-env path: direct call (also handles direct-env
+            # providers like groq/ or bedrock/ that are not in the Router
+            # model_list even when channel mode is active)
+            response = call_litellm_with_param_recovery(
+                lambda kwargs: litellm.completion(**kwargs),
+                model=model,
+                call_kwargs=call_kwargs,
+                model_list=self._config.llm_model_list,
+                logger=logger,
+            )

        return self._parse_litellm_response(response, model)

--- a/src/analyzer.py
+++ b/src/analyzer.py
@@ -30,9 +30,10 @@ from src.config import (
    get_api_keys_for_model,
    get_config,
    get_configured_llm_models,
-    normalize_litellm_temperature,
    resolve_news_window_days,
 )
+from src.llm.generation_params import apply_litellm_generation_params
+from src.llm.errors import call_litellm_with_param_recovery
 from src.storage import persist_llm_usage
 from src.data.stock_mapping import STOCK_NAME_MAP
 from src.report_language import (
@@ -1810,6 +1811,7 @@ class GeminiAnalyzer:
        self._use_legacy_default_prompt_override = use_legacy_default_prompt
        self._resolved_prompt_state: Optional[Dict[str, Any]] = None
        self._router = None
+        self._legacy_router_model_list: List[Dict[str, Any]] = []
        self._litellm_available = False
        self._init_litellm()
        if not self._litellm_available:
@@ -1908,6 +1910,47 @@ class GeminiAnalyzer:
            e.get('model_name', '').startswith('__legacy_') for e in config.llm_model_list
        )

+    @staticmethod
+    def _legacy_router_provider_alias(model: str) -> str:
+        provider = model.split("/", 1)[0] if "/" in model else "openai"
+        return f"__legacy_{provider}__"
+
+    @staticmethod
+    def _build_legacy_router_model_list_from_config(
+        model: str,
+        model_list: List[Dict[str, Any]],
+    ) -> List[Dict[str, Any]]:
+        """Build legacy-router candidates from configured legacy llm_model_list entries."""
+        if not model:
+            return []
+        target_model = model
+        target_legacy_alias = GeminiAnalyzer._legacy_router_provider_alias(model)
+        legacy_entries: List[Dict[str, Any]] = []
+        for entry in model_list or []:
+            if not isinstance(entry, dict):
+                continue
+            model_name = str(entry.get("model_name") or "").strip()
+            if model_name != target_legacy_alias:
+                continue
+
+            params = entry.get("litellm_params")
+            if not isinstance(params, dict):
+                continue
+
+            api_key = str(params.get("api_key") or "").strip()
+            if not api_key or len(api_key) < 8:
+                continue
+
+            deployed_params = dict(params)
+            deployed_params["model"] = target_model
+            deployed_params["api_key"] = api_key
+            legacy_entries.append({
+                "model_name": target_model,
+                "litellm_params": deployed_params,
+            })
+
+        return legacy_entries
+
    def _init_litellm(self) -> None:
        """Initialize litellm Router from channels / YAML / legacy keys."""
        config = self._get_runtime_config()
@@ -1921,27 +1964,34 @@ class GeminiAnalyzer:
        # --- Channel / YAML path: build Router from pre-built model_list ---
        if self._has_channel_config(config):
            model_list = config.llm_model_list
-            self._router = Router(
-                model_list=model_list,
-                routing_strategy="simple-shuffle",
-                num_retries=2,
-            )
-            unique_models = list(dict.fromkeys(
-                e['litellm_params']['model'] for e in model_list
-            ))
-            logger.info(
-                f"Analyzer LLM: Router initialized from channels/YAML — "
-                f"{len(model_list)} deployment(s), models: {unique_models}"
-            )
-            return
+            try:
+                self._router = Router(
+                    model_list=model_list,
+                    routing_strategy="simple-shuffle",
+                    num_retries=2,
+                )
+            except TypeError:
+                logger.debug("Analyzer LLM: Router constructor signature not compatible; fallback to direct mode")
+                self._router = None
+            else:
+                unique_models = list(dict.fromkeys(
+                    e['litellm_params']['model'] for e in model_list
+                ))
+                logger.info(
+                    f"Analyzer LLM: Router initialized from channels/YAML — "
+                    f"{len(model_list)} deployment(s), models: {unique_models}"
+                )
+                return

        # --- Legacy path: build Router for multi-key, or use single key ---
        keys = get_api_keys_for_model(litellm_model, config)
-
-        if len(keys) > 1:
-            # Build legacy Router for primary model multi-key load-balancing
+        legacy_model_list = self._build_legacy_router_model_list_from_config(
+            litellm_model,
+            config.llm_model_list,
+        )
+        if len(legacy_model_list) <= 1 and keys:
            extra_params = extra_litellm_params(litellm_model, config)
-            legacy_model_list = [
+            configured_model_list = [
                {
                    "model_name": litellm_model,
                    "litellm_params": {
@@ -1952,16 +2002,30 @@ class GeminiAnalyzer:
                }
                for k in keys
            ]
-            self._router = Router(
-                model_list=legacy_model_list,
-                routing_strategy="simple-shuffle",
-                num_retries=2,
-            )
-            logger.info(
-                f"Analyzer LLM: Legacy Router initialized with {len(keys)} keys "
-                f"for {litellm_model}"
-            )
-        elif keys:
+            if not legacy_model_list:
+                legacy_model_list = configured_model_list
+            elif len(legacy_model_list) < len(configured_model_list):
+                legacy_model_list = configured_model_list
+
+        if len(legacy_model_list) > 1:
+            self._legacy_router_model_list = legacy_model_list
+            try:
+                self._router = Router(
+                    model_list=legacy_model_list,
+                    routing_strategy="simple-shuffle",
+                    num_retries=2,
+                )
+            except TypeError:
+                logger.debug("Analyzer LLM: Legacy Router constructor signature not compatible; using legacy model_list fallback")
+                self._router = None
+            else:
+                logger.info(
+                    f"Analyzer LLM: Legacy Router initialized with {len(legacy_model_list)} keys "
+                    f"for {litellm_model}"
+                )
+                return
+
+        if keys:
            logger.info(f"Analyzer LLM: litellm initialized (model={litellm_model})")
        else:
            logger.info(
@@ -2206,6 +2270,11 @@ class GeminiAnalyzer:
        effective_system_prompt = system_prompt or self.TEXT_SYSTEM_PROMPT
        router_model_names = set(get_configured_llm_models(config.llm_model_list))
        for model in models_to_try:
+            recovery_model_list = config.llm_model_list
+            legacy_router_model_list = getattr(self, "_legacy_router_model_list", None) or []
+            if legacy_router_model_list and model == config.litellm_model and not use_channel_router:
+                recovery_model_list = legacy_router_model_list
+
            try:
                model_short = model.split("/")[-1] if "/" in model else model
                extra = get_thinking_extra_body(model_short)
@@ -2215,28 +2284,50 @@ class GeminiAnalyzer:
                        {"role": "system", "content": effective_system_prompt},
                        {"role": "user", "content": prompt},
                    ],
-                    "temperature": normalize_litellm_temperature(
-                        model,
-                        requested_temperature,
-                        model_list=config.llm_model_list,
-                        request_overrides={"extra_body": extra} if extra else None,
-                    ),
                    "max_tokens": max_tokens,
                }
                if extra:
                    call_kwargs["extra_body"] = extra
+                uses_router = (
+                    (use_channel_router and self._router and model in router_model_names)
+                    or (self._router and model == config.litellm_model and not use_channel_router)
+                )
+                if not uses_router:
+                    try:
+                        keys = get_api_keys_for_model(model, config)
+                    except AttributeError:
+                        keys = []
+                    if keys:
+                        call_kwargs["api_key"] = keys[0]
+                    try:
+                        call_kwargs.update(extra_litellm_params(model, config))
+                    except AttributeError:
+                        pass
+                call_kwargs = apply_litellm_generation_params(
+                    call_kwargs,
+                    model,
+                    requested_temperature,
+                    model_list=recovery_model_list,
+                )

                _stream_text: Optional[str] = None
                _stream_usage: Dict[str, Any] = {}

                if stream:
                    try:
-                        stream_response = self._dispatch_litellm_completion(
-                            model,
-                            {**call_kwargs, "stream": True},
-                            config=config,
-                            use_channel_router=use_channel_router,
-                            router_model_names=router_model_names,
+                        stream_response = call_litellm_with_param_recovery(
+                            lambda kwargs: self._dispatch_litellm_completion(
+                                model,
+                                kwargs,
+                                config=config,
+                                use_channel_router=use_channel_router,
+                                router_model_names=router_model_names,
+                            ),
+                            model=model,
+                            call_kwargs={**call_kwargs, "stream": True},
+                            model_list=recovery_model_list,
+                            cache_recovery=False,
+                            logger=logger,
                        )
                        _stream_text, _stream_usage = self._consume_litellm_stream(
                            stream_response,
@@ -2272,12 +2363,18 @@ class GeminiAnalyzer:
                        response_validator(_stream_text)
                    return _stream_text, model, _stream_usage

-                response = self._dispatch_litellm_completion(
-                    model,
-                    call_kwargs,
-                    config=config,
-                    use_channel_router=use_channel_router,
-                    router_model_names=router_model_names,
+                response = call_litellm_with_param_recovery(
+                    lambda kwargs: self._dispatch_litellm_completion(
+                        model,
+                        kwargs,
+                        config=config,
+                        use_channel_router=use_channel_router,
+                        router_model_names=router_model_names,
+                    ),
+                    model=model,
+                    call_kwargs=call_kwargs,
+                    model_list=recovery_model_list,
+                    logger=logger,
                )

                content = self._extract_completion_text(response)
--- a/src/config.py
+++ b/src/config.py
@@ -31,6 +31,7 @@ from src.notification_noise import (
    parse_notification_quiet_hours,
    validate_notification_timezone,
 )
+from src.llm import generation_params as llm_generation_params

 logger = logging.getLogger(__name__)

@@ -61,20 +62,6 @@ _FALSEY_ENV_VALUES = {"0", "false", "no", "off"}
 # These are compatibility examples; actual availability should be validated by Anspire console/model entitlement.
 ANSPIRE_LLM_BASE_URL_DEFAULT = "https://open-gateway.anspire.cn/v6"
 ANSPIRE_LLM_MODEL_DEFAULT = "Doubao-Seed-2.0-lite"
-# Kimi K2.6 is consumed through Moonshot's OpenAI-compatible API in this
-# repository. Official references:
-# - https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart
-# - https://platform.moonshot.ai/docs/guide/compatibility#parameters-differences-in-request-body
-# - https://huggingface.co/moonshotai/Kimi-K2.6
-# - https://docs.litellm.ai/docs/providers/openai_compatible
-# Only the strict Kimi K2.6 family is normalized here; other models and
-# fallbacks continue using the configured runtime temperature.
-_FIXED_TEMPERATURE_LITELLM_MODELS: Dict[str, Dict[str, float]] = {
-    "kimi-k2.6": {
-        "thinking": 1.0,
-        "non_thinking": 0.6,
-    },
-}


 def _has_ntfy_topic_endpoint(value: Optional[str]) -> bool:
@@ -350,71 +337,7 @@ def resolve_litellm_wire_model(
    model_list: Optional[List[Dict[str, Any]]] = None,
 ) -> str:
    """Resolve a router alias to its underlying LiteLLM wire model."""
-    normalized_model = (model or "").strip()
-    if not normalized_model or not model_list:
-        return normalized_model
-
-    model_entry = _resolve_litellm_model_list_entry(normalized_model, model_list)
-    if not model_entry:
-        return normalized_model
-
-    params = model_entry.get("litellm_params", {}) or {}
-    wire_model = str(params.get("model") or "").strip()
-    if wire_model:
-        return wire_model
-    return normalized_model
-
-
-def _resolve_litellm_model_list_entry(
-    model: str,
-    model_list: Optional[List[Dict[str, Any]]] = None,
-) -> Optional[Dict[str, Any]]:
-    """Return the Router model_list entry matching the configured alias."""
-    normalized_model = (model or "").strip()
-    if not normalized_model or not model_list:
-        return None
-
-    for entry in model_list:
-        model_name = str(entry.get("model_name") or "").strip()
-        if not model_name:
-            params = entry.get("litellm_params", {}) or {}
-            model_name = str(params.get("model") or "").strip()
-        if model_name == normalized_model:
-            return entry
-    return None
-
-
-def _extract_thinking_config(payload: Optional[Dict[str, Any]]) -> Any:
-    """Extract a thinking-mode flag from LiteLLM-style request kwargs."""
-    if not isinstance(payload, dict):
-        return None
-    extra_body = payload.get("extra_body")
-    if isinstance(extra_body, dict) and "thinking" in extra_body:
-        return extra_body.get("thinking")
-    if "thinking" in payload:
-        return payload.get("thinking")
-    return None
-
-
-def _parse_thinking_enabled(value: Any) -> Optional[bool]:
-    """Parse thinking-mode config into True/False/unknown."""
-    if value is None:
-        return None
-    if isinstance(value, bool):
-        return value
-    if isinstance(value, str):
-        normalized = value.strip().lower()
-        if normalized in {"enabled", "enable", "true", "1", "on", "thinking"}:
-            return True
-        if normalized in {"disabled", "disable", "false", "0", "off", "none", "non-thinking", "non_thinking"}:
-            return False
-        return None
-    if isinstance(value, dict):
-        if "enabled" in value:
-            return _parse_thinking_enabled(value.get("enabled"))
-        if "type" in value:
-            return _parse_thinking_enabled(value.get("type"))
-    return None
+    return llm_generation_params.resolve_litellm_wire_model(model, model_list)


 def resolve_litellm_thinking_enabled(
@@ -423,19 +346,11 @@ def resolve_litellm_thinking_enabled(
    request_overrides: Optional[Dict[str, Any]] = None,
 ) -> Optional[bool]:
    """Resolve whether the outgoing LiteLLM request explicitly enables thinking."""
-    thinking_config = None
-    model_entry = _resolve_litellm_model_list_entry(model, model_list)
-    if model_entry:
-        thinking_config = _extract_thinking_config(model_entry)
-        entry_params = model_entry.get("litellm_params", {}) or {}
-        entry_thinking_config = _extract_thinking_config(entry_params)
-        if entry_thinking_config is not None:
-            thinking_config = entry_thinking_config
-
-    override_thinking_config = _extract_thinking_config(request_overrides)
-    if override_thinking_config is not None:
-        thinking_config = override_thinking_config
-    return _parse_thinking_enabled(thinking_config)
+    return llm_generation_params.resolve_litellm_thinking_enabled(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )


 def get_fixed_litellm_temperature(
@@ -444,24 +359,11 @@ def get_fixed_litellm_temperature(
    request_overrides: Optional[Dict[str, Any]] = None,
 ) -> Optional[float]:
    """Return a provider-mandated temperature for known strict models."""
-    normalized_model = resolve_litellm_wire_model(model, model_list).lower()
-    if not normalized_model:
-        return None
-    thinking_enabled = resolve_litellm_thinking_enabled(
+    return llm_generation_params.get_fixed_litellm_temperature(
        model,
        model_list=model_list,
        request_overrides=request_overrides,
    )
-    model_parts = [part for part in re.split(r"[/:\s]+", normalized_model) if part]
-    for model_name, temperatures in _FIXED_TEMPERATURE_LITELLM_MODELS.items():
-        if any(part == model_name or part.startswith(f"{model_name}-") for part in model_parts):
-            if thinking_enabled is False and temperatures.get("non_thinking") is not None:
-                return temperatures["non_thinking"]
-            if temperatures.get("thinking") is not None:
-                return temperatures["thinking"]
-            if temperatures.get("non_thinking") is not None:
-                return temperatures["non_thinking"]
-    return None


 def normalize_litellm_temperature(
@@ -473,16 +375,13 @@ def normalize_litellm_temperature(
    request_overrides: Optional[Dict[str, Any]] = None,
 ) -> float:
    """Normalize temperature before sending a LiteLLM request."""
-    fixed_temperature = get_fixed_litellm_temperature(
+    return llm_generation_params.normalize_litellm_temperature(
        model,
+        temperature,
+        default=default,
        model_list=model_list,
        request_overrides=request_overrides,
    )
-    if fixed_temperature is not None:
-        return fixed_temperature
-    if temperature is None:
-        return default
-    return float(temperature)


 def resolve_unified_llm_temperature(model: str) -> float:
--- a/src/llm/init.py
+++ b/src/llm/init.py
@@ -0,0 +1,2 @@
+"""LLM runtime helpers."""
+
--- a/src/llm/errors.py
+++ b/src/llm/errors.py
@@ -0,0 +1,151 @@
+# -*- coding: utf-8 -*-
+"""LiteLLM error classification and one-shot parameter recovery."""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Callable, Dict, List, Optional
+
+from src.llm.generation_params import (
+    GenerationParamRecovery,
+    apply_litellm_param_recovery,
+    remember_litellm_generation_param_recovery,
+)
+
+_UNSUPPORTED_PARAM_MARKERS = (
+    "unsupported",
+    "not supported",
+    "unrecognized",
+    "unknown parameter",
+    "not allowed",
+    "invalid parameter",
+    "does not support",
+)
+
+_TEMPERATURE_VALUE_PATTERN = r"-?\d+(?:\.\d+)?"
+_ALLOWED_TEMPERATURE_PATTERNS = (
+    re.compile(
+        rf"\bonly\s+(?:the\s+)?(?:default\s+)?(?:temperature\s+)?(?:value\s+)?[\(`'\"]*(?P<value>{_TEMPERATURE_VALUE_PATTERN})(?!\w)"
+    ),
+    re.compile(
+        rf"\bdefault(?:\s+temperature)?(?:\s+value)?\s*(?:is|=|:)\s*[\(`'\"]*(?P<value>{_TEMPERATURE_VALUE_PATTERN})(?!\w)"
+    ),
+)
+
+
+def _collect_error_text(value: Any, seen: Optional[set] = None) -> List[str]:
+    if seen is None:
+        seen = set()
+    if value is None:
+        return []
+    value_id = id(value)
+    if value_id in seen:
+        return []
+    seen.add(value_id)
+
+    chunks = [str(value)]
+    if isinstance(value, BaseException):
+        chunks.extend(_collect_error_text(getattr(value, "args", None), seen))
+    if isinstance(value, dict):
+        for item in value.values():
+            chunks.extend(_collect_error_text(item, seen))
+    elif isinstance(value, (list, tuple, set)):
+        for item in value:
+            chunks.extend(_collect_error_text(item, seen))
+    else:
+        for attr in ("message", "body", "response", "llm_provider", "param"):
+            if hasattr(value, attr):
+                chunks.extend(_collect_error_text(getattr(value, attr), seen))
+    return chunks
+
+
+def _normalized_error_text(error: BaseException) -> str:
+    return " ".join(chunk for chunk in _collect_error_text(error) if chunk).lower()
+
+
+def _parse_allowed_temperature(text: str) -> Optional[float]:
+    for segment in re.split(r"(?<!\d)\.(?!\d)|[!?;\n]+", text):
+        if "only" not in segment:
+            continue
+        for pattern in _ALLOWED_TEMPERATURE_PATTERNS:
+            match = pattern.search(segment[segment.find("only") :])
+            if match is None:
+                continue
+            value = float(match.group("value"))
+            if 0 <= value <= 2:
+                return value
+    return None
+
+
+def classify_litellm_generation_param_error(
+    error: BaseException,
+) -> Optional[GenerationParamRecovery]:
+    """Classify explicit provider parameter errors into a safe one-shot recovery."""
+    text = _normalized_error_text(error)
+    if not text:
+        return None
+
+    if "temperature" in text:
+        allowed_temperature = _parse_allowed_temperature(text)
+        if allowed_temperature is not None:
+            return GenerationParamRecovery(
+                set_params={"temperature": allowed_temperature},
+                reason="temperature_default_only",
+            )
+        if "only" in text and "default" in text:
+            return GenerationParamRecovery(
+                omit_params=("temperature",),
+                reason="temperature_default_only",
+            )
+        if any(marker in text for marker in _UNSUPPORTED_PARAM_MARKERS):
+            return GenerationParamRecovery(
+                omit_params=("temperature",),
+                reason="temperature_unsupported",
+            )
+
+    for param in ("top_p", "presence_penalty", "frequency_penalty", "seed"):
+        if param in text and any(marker in text for marker in _UNSUPPORTED_PARAM_MARKERS):
+            return GenerationParamRecovery(
+                omit_params=(param,),
+                reason=f"{param}_unsupported",
+            )
+    return None
+
+
+def call_litellm_with_param_recovery(
+    call: Callable[[Dict[str, Any]], Any],
+    *,
+    model: str,
+    call_kwargs: Dict[str, Any],
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    cache_recovery: bool = True,
+    logger: Optional[Any] = None,
+    log_label: str = "[LiteLLM]",
+) -> Any:
+    """Call LiteLLM once, then retry once for explicit generation-parameter errors."""
+    effective_kwargs = dict(call_kwargs)
+    try:
+        return call(effective_kwargs)
+    except Exception as exc:
+        recovery = classify_litellm_generation_param_error(exc)
+        if recovery is None:
+            raise
+        retry_kwargs = apply_litellm_param_recovery(effective_kwargs, recovery)
+        if retry_kwargs == effective_kwargs:
+            raise
+        if logger is not None:
+            logger.warning(
+                "%s %s generation parameter rejected (%s), retrying once with request-scoped recovery",
+                log_label,
+                model,
+                recovery.reason,
+            )
+        response = call(retry_kwargs)
+        if cache_recovery:
+            remember_litellm_generation_param_recovery(
+                model,
+                recovery,
+                model_list=model_list,
+                request_overrides=retry_kwargs,
+            )
+        return response
--- a/src/llm/generation_params.py
+++ b/src/llm/generation_params.py
@@ -0,0 +1,439 @@
+# -*- coding: utf-8 -*-
+"""LiteLLM generation-parameter compatibility helpers."""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Mapping, Optional, Tuple
+
+
+# Kimi K2.6 is consumed through Moonshot's OpenAI-compatible API in this
+# repository. Official references:
+# - https://platform.kimi.ai/docs/guide/kimi-k2-6-quickstart
+# - https://platform.moonshot.ai/docs/guide/compatibility#parameters-differences-in-request-body
+# - https://huggingface.co/moonshotai/Kimi-K2.6
+# - https://docs.litellm.ai/docs/providers/openai_compatible
+_FIXED_TEMPERATURE_LITELLM_MODELS: Dict[str, Dict[str, float]] = {
+    "kimi-k2.6": {
+        "thinking": 1.0,
+        "non_thinking": 0.6,
+    },
+}
+
+
+@dataclass(frozen=True)
+class TemperatureDirective:
+    """Request-scoped temperature strategy for one LiteLLM model call."""
+
+    temperature: Optional[float] = None
+    omit_temperature: bool = False
+    reason: str = ""
+
+
+@dataclass(frozen=True)
+class GenerationParamRecovery:
+    """A learned request-parameter repair for a LiteLLM model call."""
+
+    omit_params: Tuple[str, ...] = ()
+    set_params: Mapping[str, Any] = field(default_factory=dict)
+    reason: str = ""
+
+
+_GENERATION_PARAM_RECOVERY_CACHE: Dict[str, GenerationParamRecovery] = {}
+
+_LITELLM_ENDPOINT_PARAM_KEYS = (
+    "api_base",
+    "base_url",
+    "api_version",
+    "api_type",
+    "azure_endpoint",
+    "azure_deployment",
+    "deployment_id",
+    "custom_llm_provider",
+    "organization",
+    "region_name",
+    "aws_region_name",
+    "vertex_project",
+    "vertex_location",
+    "extra_headers",
+    "headers",
+    "default_headers",
+)
+_LITELLM_ROUTING_PARAM_KEYS = ("model", *_LITELLM_ENDPOINT_PARAM_KEYS)
+_SECRET_CACHE_FIELD_NAMES = {
+    "api_key",
+    "authorization",
+    "proxy_authorization",
+    "cookie",
+    "set-cookie",
+    "x-api-key",
+    "api-key",
+    "openai-api-key",
+}
+
+
+def _resolve_litellm_model_list_entry(
+    model: str,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+) -> Optional[Dict[str, Any]]:
+    """Return the Router model_list entry matching the configured alias."""
+    entries = _resolve_litellm_model_list_entries(model, model_list)
+    return entries[0] if entries else None
+
+
+def _resolve_litellm_model_list_entries(
+    model: str,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+) -> List[Dict[str, Any]]:
+    """Return Router model_list entries matching the configured alias."""
+    normalized_model = (model or "").strip()
+    if not normalized_model or not model_list:
+        return []
+
+    entries: List[Dict[str, Any]] = []
+    for entry in model_list:
+        model_name = str(entry.get("model_name") or "").strip()
+        if not model_name:
+            params = entry.get("litellm_params", {}) or {}
+            model_name = str(params.get("model") or "").strip()
+        if model_name == normalized_model:
+            entries.append(entry)
+    return entries
+
+
+def resolve_litellm_wire_model(
+    model: str,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+) -> str:
+    """Resolve a router alias to its underlying LiteLLM wire model."""
+    normalized_model = (model or "").strip()
+    if not normalized_model or not model_list:
+        return normalized_model
+
+    model_entry = _resolve_litellm_model_list_entry(normalized_model, model_list)
+    if not model_entry:
+        return normalized_model
+
+    params = model_entry.get("litellm_params", {}) or {}
+    wire_model = str(params.get("model") or "").strip()
+    if wire_model:
+        return wire_model
+    return normalized_model
+
+
+def _extract_thinking_config(payload: Optional[Dict[str, Any]]) -> Any:
+    """Extract a thinking-mode flag from LiteLLM-style request kwargs."""
+    if not isinstance(payload, dict):
+        return None
+    extra_body = payload.get("extra_body")
+    if isinstance(extra_body, dict) and "thinking" in extra_body:
+        return extra_body.get("thinking")
+    if "thinking" in payload:
+        return payload.get("thinking")
+    return None
+
+
+def _parse_thinking_enabled(value: Any) -> Optional[bool]:
+    """Parse thinking-mode config into True/False/unknown."""
+    if value is None:
+        return None
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        normalized = value.strip().lower()
+        if normalized in {"enabled", "enable", "true", "1", "on", "thinking"}:
+            return True
+        if normalized in {"disabled", "disable", "false", "0", "off", "none", "non-thinking", "non_thinking"}:
+            return False
+        return None
+    if isinstance(value, dict):
+        if "enabled" in value:
+            return _parse_thinking_enabled(value.get("enabled"))
+        if "type" in value:
+            return _parse_thinking_enabled(value.get("type"))
+    return None
+
+
+def resolve_litellm_thinking_enabled(
+    model: str,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> Optional[bool]:
+    """Resolve whether the outgoing LiteLLM request explicitly enables thinking."""
+    thinking_config = None
+    model_entry = _resolve_litellm_model_list_entry(model, model_list)
+    if model_entry:
+        thinking_config = _extract_thinking_config(model_entry)
+        entry_params = model_entry.get("litellm_params", {}) or {}
+        entry_thinking_config = _extract_thinking_config(entry_params)
+        if entry_thinking_config is not None:
+            thinking_config = entry_thinking_config
+
+    override_thinking_config = _extract_thinking_config(request_overrides)
+    if override_thinking_config is not None:
+        thinking_config = override_thinking_config
+    return _parse_thinking_enabled(thinking_config)
+
+
+def _model_parts(model: str) -> List[str]:
+    return [part for part in re.split(r"[/:\s]+", (model or "").lower()) if part]
+
+
+def _matches_model_family(model: str, family: str) -> bool:
+    return any(part == family or part.startswith(f"{family}-") for part in _model_parts(model))
+
+
+def _should_omit_litellm_temperature(model: str) -> bool:
+    """Return whether a model family should rely on the provider default temperature."""
+    return any(
+        part.startswith(("gpt-5", "gpt5"))
+        or part in {"o1", "o3", "o4"}
+        or part.startswith(("o1-", "o3-", "o4-"))
+        for part in _model_parts(model)
+    )
+
+
+def get_fixed_litellm_temperature(
+    model: str,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> Optional[float]:
+    """Return a provider-mandated temperature for known strict models."""
+    normalized_model = resolve_litellm_wire_model(model, model_list).lower()
+    if not normalized_model:
+        return None
+    thinking_enabled = resolve_litellm_thinking_enabled(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )
+    for model_name, temperatures in _FIXED_TEMPERATURE_LITELLM_MODELS.items():
+        if _matches_model_family(normalized_model, model_name):
+            if thinking_enabled is False and temperatures.get("non_thinking") is not None:
+                return temperatures["non_thinking"]
+            if temperatures.get("thinking") is not None:
+                return temperatures["thinking"]
+            if temperatures.get("non_thinking") is not None:
+                return temperatures["non_thinking"]
+    return None
+
+
+def resolve_litellm_temperature_directive(
+    model: str,
+    *,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> TemperatureDirective:
+    """Resolve the request-scoped temperature directive for a LiteLLM model."""
+    fixed_temperature = get_fixed_litellm_temperature(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )
+    if fixed_temperature is not None:
+        return TemperatureDirective(
+            temperature=fixed_temperature,
+            reason="fixed_model_temperature",
+        )
+
+    wire_model = resolve_litellm_wire_model(model, model_list)
+    if _should_omit_litellm_temperature(wire_model):
+        return TemperatureDirective(
+            omit_temperature=True,
+            reason="provider_default_temperature",
+        )
+    return TemperatureDirective()
+
+
+def normalize_litellm_temperature(
+    model: str,
+    temperature: Optional[float],
+    *,
+    default: float = 0.7,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> float:
+    """Return the legacy float temperature normalization for callers that need it."""
+    fixed_temperature = get_fixed_litellm_temperature(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )
+    if fixed_temperature is not None:
+        return fixed_temperature
+    if temperature is None:
+        return default
+    return float(temperature)
+
+
+def _redact_recovery_cache_value(param_name: str, value: Any) -> Any:
+    if param_name.strip().lower() in _SECRET_CACHE_FIELD_NAMES:
+        return "<set>" if value else "<empty>"
+    if isinstance(value, Mapping):
+        return {
+            str(key): _redact_recovery_cache_value(str(key), nested_value)
+            for key, nested_value in sorted(value.items(), key=lambda item: str(item[0]))
+        }
+    if isinstance(value, (list, tuple)):
+        return [_redact_recovery_cache_value(param_name, item) for item in value]
+    return value
+
+
+def _stable_recovery_cache_json(value: Mapping[str, Any]) -> str:
+    redacted = {
+        key: _redact_recovery_cache_value(key, val)
+        for key, val in sorted(value.items())
+    }
+    return json.dumps(redacted, sort_keys=True, separators=(",", ":"), default=str)
+
+
+def _filter_litellm_routing_params(params: Mapping[str, Any]) -> Dict[str, Any]:
+    return {
+        key: params[key]
+        for key in _LITELLM_ROUTING_PARAM_KEYS
+        if key in params and params[key] not in (None, "")
+    }
+
+
+def _request_endpoint_cache_scope(request_overrides: Optional[Dict[str, Any]]) -> Optional[str]:
+    if not isinstance(request_overrides, Mapping):
+        return None
+    routing_params = _filter_litellm_routing_params(request_overrides)
+    if not any(key in routing_params for key in _LITELLM_ENDPOINT_PARAM_KEYS):
+        return None
+    return _stable_recovery_cache_json(routing_params)
+
+
+def _model_list_endpoint_cache_scope(
+    model: str,
+    model_list: Optional[List[Dict[str, Any]]],
+) -> Optional[str]:
+    entries = _resolve_litellm_model_list_entries(model, model_list)
+    if not entries:
+        return "default"
+
+    fingerprints = set()
+    for entry in entries:
+        params = entry.get("litellm_params", {}) or {}
+        if not isinstance(params, Mapping):
+            params = {}
+        routing_params = _filter_litellm_routing_params(params)
+        if not routing_params:
+            routing_params = {"model": str(entry.get("model_name") or model).strip()}
+        fingerprints.add(_stable_recovery_cache_json(routing_params))
+
+    if len(fingerprints) != 1:
+        return None
+    return next(iter(fingerprints))
+
+
+def _recovery_cache_key(
+    model: str,
+    *,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> Optional[str]:
+    wire_model = resolve_litellm_wire_model(model, model_list).strip().lower()
+    thinking_enabled = resolve_litellm_thinking_enabled(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )
+    endpoint_scope = _request_endpoint_cache_scope(request_overrides)
+    if endpoint_scope is None:
+        endpoint_scope = _model_list_endpoint_cache_scope(model, model_list)
+    if endpoint_scope is None:
+        return None
+    return (
+        f"{wire_model or (model or '').strip().lower()}"
+        f"|thinking={thinking_enabled}"
+        f"|endpoint={endpoint_scope}"
+    )
+
+
+def apply_litellm_param_recovery(
+    call_kwargs: Dict[str, Any],
+    recovery: GenerationParamRecovery,
+) -> Dict[str, Any]:
+    """Return kwargs with a learned parameter recovery applied."""
+    updated = dict(call_kwargs)
+    for param in recovery.omit_params:
+        updated.pop(param, None)
+    for param, value in recovery.set_params.items():
+        updated[param] = value
+    return updated
+
+
+def get_cached_litellm_generation_param_recovery(
+    model: str,
+    *,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> Optional[GenerationParamRecovery]:
+    """Return a process-local parameter recovery learned for this model call shape."""
+    key = _recovery_cache_key(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )
+    if key is None:
+        return None
+    return _GENERATION_PARAM_RECOVERY_CACHE.get(key)
+
+
+def remember_litellm_generation_param_recovery(
+    model: str,
+    recovery: GenerationParamRecovery,
+    *,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> None:
+    """Remember a successful parameter recovery for later requests in this process."""
+    key = _recovery_cache_key(
+        model,
+        model_list=model_list,
+        request_overrides=request_overrides,
+    )
+    if key is None:
+        return
+    _GENERATION_PARAM_RECOVERY_CACHE[key] = recovery
+
+
+def clear_litellm_generation_param_recovery_cache() -> None:
+    """Clear process-local learned parameter recoveries. Intended for tests."""
+    _GENERATION_PARAM_RECOVERY_CACHE.clear()
+
+
+def apply_litellm_generation_params(
+    call_kwargs: Dict[str, Any],
+    model: str,
+    temperature: Optional[float],
+    *,
+    default_temperature: float = 0.7,
+    model_list: Optional[List[Dict[str, Any]]] = None,
+    request_overrides: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    """Return kwargs with model-compatible generation parameters applied."""
+    updated = dict(call_kwargs)
+    effective_overrides = request_overrides if request_overrides is not None else updated
+    directive = resolve_litellm_temperature_directive(
+        model,
+        model_list=model_list,
+        request_overrides=effective_overrides,
+    )
+    if directive.omit_temperature:
+        updated.pop("temperature", None)
+    elif directive.temperature is not None:
+        updated["temperature"] = directive.temperature
+    else:
+        updated["temperature"] = default_temperature if temperature is None else float(temperature)
+    cached_recovery = get_cached_litellm_generation_param_recovery(
+        model,
+        model_list=model_list,
+        request_overrides=updated,
+    )
+    if cached_recovery:
+        updated = apply_litellm_param_recovery(updated, cached_recovery)
+    return updated
--- a/src/services/system_config_service.py
+++ b/src/services/system_config_service.py
@@ -27,7 +27,6 @@ from src.config import (
    channel_allows_empty_api_key,
    get_configured_llm_models,
    normalize_agent_litellm_model,
-    normalize_litellm_temperature,
    normalize_news_strategy_profile,
    normalize_llm_channel_model,
    parse_env_bool,
@@ -43,6 +42,8 @@ from src.core.config_registry import (
    get_field_definition,
    get_registered_field_keys,
 )
+from src.llm.errors import call_litellm_with_param_recovery
+from src.llm.generation_params import apply_litellm_generation_params
 from src.notification_noise import validate_notification_timezone
 from src.notification_sender.gotify_sender import resolve_gotify_message_endpoint
 from src.notification_sender.ntfy_sender import resolve_ntfy_endpoint
@@ -724,10 +725,6 @@ class SystemConfigService:
        call_kwargs: Dict[str, Any] = {
            "model": resolved_model,
            "messages": [{"role": "user", "content": "Reply with OK"}],
-            "temperature": normalize_litellm_temperature(
-                resolved_model,
-                self._get_runtime_llm_temperature(),
-            ),
            "max_tokens": 256,  # Increased to allow MiniMax-M2.7 thinking process + response
            "timeout": max(5.0, float(timeout_seconds)),
        }
@@ -735,6 +732,11 @@ class SystemConfigService:
            call_kwargs["api_key"] = selected_api_key
        if base_url.strip():
            call_kwargs["api_base"] = base_url.strip()
+        call_kwargs = apply_litellm_generation_params(
+            call_kwargs,
+            resolved_model,
+            self._get_runtime_llm_temperature(),
+        )

        try:
            import litellm
@@ -746,7 +748,13 @@ class SystemConfigService:
            LLMToolAdapter._register_custom_model_pricing()

            started_at = time.perf_counter()
-            response = litellm.completion(**call_kwargs)
+            response = call_litellm_with_param_recovery(
+                lambda kwargs: litellm.completion(**kwargs),
+                model=resolved_model,
+                call_kwargs=call_kwargs,
+                logger=logger,
+                log_label="[LLM channel test]",
+            )
            latency_ms = int((time.perf_counter() - started_at) * 1000)
            content, parse_error_code, parse_error, parse_reason = self._extract_llm_completion_content(response)
            if parse_error_code:
@@ -1151,7 +1159,6 @@ class SystemConfigService:
        call_kwargs: Dict[str, Any] = {
            "model": resolved_model,
            "messages": messages,
-            "temperature": normalize_litellm_temperature(resolved_model, 0.0),
            "max_tokens": max_tokens,
            "timeout": min(max(5.0, timeout), 10.0),
        }
@@ -1161,6 +1168,11 @@ class SystemConfigService:
            call_kwargs["api_base"] = base_url.strip()
        if extra:
            call_kwargs.update(extra)
+        call_kwargs = apply_litellm_generation_params(
+            call_kwargs,
+            resolved_model,
+            0.0,
+        )
        return call_kwargs

    @classmethod
--- a/tests/test_agent_pipeline.py
+++ b/tests/test_agent_pipeline.py
@@ -1687,6 +1687,172 @@ class TestAgentConstructionChain(unittest.TestCase):
        self.assertEqual(result.content, "agent ok")
        self.assertEqual(mock_completion.call_args.kwargs["temperature"], 0.6)

+    @patch("src.agent.llm_adapter.Router")
+    def test_llm_adapter_omits_temperature_for_gpt5_family(self, _mock_router):
+        """Agent direct LiteLLM calls should omit temperature for strict default-temperature models."""
+        mock_cfg = SimpleNamespace(
+            agent_litellm_model="",
+            litellm_model="openai/gpt5.5-ferr",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+            llm_temperature=0.2,
+            gemini_api_keys=[],
+            anthropic_api_keys=[],
+            openai_api_keys=[],
+            deepseek_api_keys=[],
+            openai_base_url=None,
+        )
+
+        from src.agent.llm_adapter import LLMToolAdapter
+        adapter = LLMToolAdapter(config=mock_cfg)
+        adapter._router = None
+        response = SimpleNamespace(
+            choices=[
+                SimpleNamespace(
+                    message=SimpleNamespace(
+                        content="agent ok",
+                        tool_calls=[],
+                    )
+                )
+            ],
+            usage=SimpleNamespace(prompt_tokens=1, completion_tokens=2, total_tokens=3),
+        )
+
+        with patch("src.agent.llm_adapter.litellm.completion", return_value=response) as mock_completion:
+            result = adapter._call_litellm_model(
+                [{"role": "user", "content": "hi"}],
+                [],
+                "openai/gpt5.5-ferr",
+                temperature=0.2,
+            )
+
+        self.assertEqual(result.content, "agent ok")
+        self.assertNotIn("temperature", mock_completion.call_args.kwargs)
+
+    @patch("src.agent.llm_adapter.Router")
+    def test_llm_adapter_recovers_from_unsupported_temperature(self, _mock_router):
+        """Agent direct LiteLLM calls should retry once with a request-scoped parameter repair."""
+        from src.llm.generation_params import clear_litellm_generation_param_recovery_cache
+
+        clear_litellm_generation_param_recovery_cache()
+        mock_cfg = SimpleNamespace(
+            agent_litellm_model="",
+            litellm_model="openai/custom-temp-locked-agent",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+            llm_temperature=0.2,
+            gemini_api_keys=[],
+            anthropic_api_keys=[],
+            openai_api_keys=[],
+            deepseek_api_keys=[],
+            openai_base_url=None,
+        )
+
+        from src.agent.llm_adapter import LLMToolAdapter
+        adapter = LLMToolAdapter(config=mock_cfg)
+        adapter._router = None
+        response = SimpleNamespace(
+            choices=[
+                SimpleNamespace(
+                    message=SimpleNamespace(
+                        content="agent ok",
+                        tool_calls=[],
+                    )
+                )
+            ],
+            usage=SimpleNamespace(prompt_tokens=1, completion_tokens=2, total_tokens=3),
+        )
+
+        with patch("src.agent.llm_adapter.litellm.completion") as mock_completion:
+            mock_completion.side_effect = [
+                RuntimeError("Unsupported parameter: temperature is not supported"),
+                response,
+            ]
+            result = adapter._call_litellm_model(
+                [{"role": "user", "content": "hi"}],
+                [],
+                "openai/custom-temp-locked-agent",
+                temperature=0.2,
+            )
+
+        self.assertEqual(result.content, "agent ok")
+        self.assertEqual(mock_completion.call_args_list[0].kwargs["temperature"], 0.2)
+        self.assertNotIn("temperature", mock_completion.call_args_list[1].kwargs)
+
+    @patch("src.agent.llm_adapter.Router")
+    def test_llm_adapter_legacy_router_recovery_cache_is_scoped_to_endpoint(self, mock_router):
+        """Legacy multi-key Router recoveries should not leak across base URLs."""
+        from src.llm.generation_params import clear_litellm_generation_param_recovery_cache
+
+        clear_litellm_generation_param_recovery_cache()
+        response = SimpleNamespace(
+            choices=[
+                SimpleNamespace(
+                    message=SimpleNamespace(
+                        content="agent ok",
+                        tool_calls=[],
+                    )
+                )
+            ],
+            usage=SimpleNamespace(prompt_tokens=1, completion_tokens=2, total_tokens=3),
+        )
+        strict_router = MagicMock()
+        flex_router = MagicMock()
+        strict_router.completion.side_effect = [
+            RuntimeError("Unsupported parameter: temperature is not supported"),
+            response,
+        ]
+        flex_router.completion.return_value = response
+        mock_router.side_effect = [strict_router, flex_router]
+
+        strict_cfg = SimpleNamespace(
+            agent_litellm_model="",
+            litellm_model="openai/shared-model",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+            llm_temperature=0.2,
+            gemini_api_keys=[],
+            anthropic_api_keys=[],
+            openai_api_keys=["sk-strict-key-1", "sk-strict-key-2"],
+            deepseek_api_keys=[],
+            openai_base_url="https://strict.example/v1",
+        )
+        flex_cfg = SimpleNamespace(
+            agent_litellm_model="",
+            litellm_model="openai/shared-model",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+            llm_temperature=0.2,
+            gemini_api_keys=[],
+            anthropic_api_keys=[],
+            openai_api_keys=["sk-flex-key-1", "sk-flex-key-2"],
+            deepseek_api_keys=[],
+            openai_base_url="https://flex.example/v1",
+        )
+
+        from src.agent.llm_adapter import LLMToolAdapter
+
+        strict_adapter = LLMToolAdapter(config=strict_cfg)
+        strict_result = strict_adapter._call_litellm_model(
+            [{"role": "user", "content": "hi"}],
+            [],
+            "openai/shared-model",
+            temperature=0.2,
+        )
+        flex_adapter = LLMToolAdapter(config=flex_cfg)
+        flex_result = flex_adapter._call_litellm_model(
+            [{"role": "user", "content": "hi"}],
+            [],
+            "openai/shared-model",
+            temperature=0.2,
+        )
+
+        self.assertEqual(strict_result.content, "agent ok")
+        self.assertEqual(flex_result.content, "agent ok")
+        self.assertEqual(strict_router.completion.call_args_list[0].kwargs["temperature"], 0.2)
+        self.assertNotIn("temperature", strict_router.completion.call_args_list[1].kwargs)
+        self.assertEqual(flex_router.completion.call_args.kwargs["temperature"], 0.2)
+
    @patch("src.agent.llm_adapter.Router")
    def test_llm_adapter_fallback_does_not_leak_kimi_fixed_temperature(self, _mock_router):
        """Non-Kimi fallbacks should keep the requested temperature after a Kimi failure."""
--- a/tests/test_llm_channel_config.py
+++ b/tests/test_llm_channel_config.py
@@ -14,6 +14,10 @@ from src.config import (
    get_fixed_litellm_temperature,
    normalize_litellm_temperature,
 )
+from src.llm.generation_params import (
+    apply_litellm_generation_params,
+    resolve_litellm_temperature_directive,
+)


 class LLMChannelConfigTestCase(unittest.TestCase):
@@ -479,6 +483,38 @@ class LLMChannelConfigTestCase(unittest.TestCase):
            0.6,
        )

+    def test_gpt5_family_temperature_is_omitted_at_request_build_time(self) -> None:
+        directive = resolve_litellm_temperature_directive("openai/gpt5.5-ferr")
+        self.assertTrue(directive.omit_temperature)
+
+        call_kwargs = apply_litellm_generation_params(
+            {"model": "openai/gpt5.5-ferr", "messages": [], "temperature": 0.2},
+            "openai/gpt5.5-ferr",
+            0.2,
+        )
+
+        self.assertNotIn("temperature", call_kwargs)
+        self.assertAlmostEqual(normalize_litellm_temperature("openai/gpt5.5-ferr", 0.2), 0.2)
+
+    def test_gpt5_temperature_directive_resolves_litellm_yaml_alias(self) -> None:
+        model_list = [
+            {
+                "model_name": "future_router",
+                "litellm_params": {"model": "openai/gpt-5.5"},
+            }
+        ]
+
+        directive = resolve_litellm_temperature_directive("future_router", model_list=model_list)
+        call_kwargs = apply_litellm_generation_params(
+            {"model": "future_router", "messages": []},
+            "future_router",
+            0.2,
+            model_list=model_list,
+        )
+
+        self.assertTrue(directive.omit_temperature)
+        self.assertNotIn("temperature", call_kwargs)
+
    @patch("src.config.setup_env")
    @patch.object(Config, "_parse_litellm_yaml", return_value=[])
    def test_local_openai_compatible_channel_defaults_to_openai_protocol(self, _mock_parse_yaml, _mock_setup_env) -> None:
--- a/tests/test_llm_param_recovery.py
+++ b/tests/test_llm_param_recovery.py
@@ -0,0 +1,202 @@
+# -*- coding: utf-8 -*-
+"""Tests for LiteLLM generation-parameter recovery."""
+
+from src.llm.errors import (
+    call_litellm_with_param_recovery,
+    classify_litellm_generation_param_error,
+)
+from src.llm.generation_params import (
+    apply_litellm_generation_params,
+    clear_litellm_generation_param_recovery_cache,
+)
+
+
+def test_temperature_default_only_error_sets_temperature_to_one() -> None:
+    recovery = classify_litellm_generation_param_error(
+        RuntimeError(
+            "Unsupported value: 'temperature' does not support 0.7 with this model. "
+            "Only the default (1.0) value is supported."
+        )
+    )
+
+    assert recovery is not None
+    assert recovery.set_params == {"temperature": 1.0}
+    assert recovery.omit_params == ()
+
+
+def test_temperature_default_only_error_uses_named_default_value() -> None:
+    recovery = classify_litellm_generation_param_error(
+        RuntimeError(
+            "Unsupported value: 'temperature' does not support 1.0 with this model. "
+            "Only `0.6` is allowed."
+        )
+    )
+
+    assert recovery is not None
+    assert recovery.set_params == {"temperature": 0.6}
+    assert recovery.omit_params == ()
+
+
+def test_temperature_default_only_error_without_named_value_omits_temperature() -> None:
+    recovery = classify_litellm_generation_param_error(
+        RuntimeError(
+            "Unsupported value: 'temperature' does not support 0.7 with this model. "
+            "Only the default value is supported."
+        )
+    )
+
+    assert recovery is not None
+    assert recovery.set_params == {}
+    assert recovery.omit_params == ("temperature",)
+
+
+def test_unsupported_temperature_error_retries_once_and_caches_recovery() -> None:
+    clear_litellm_generation_param_recovery_cache()
+    calls = []
+
+    def _call(kwargs):
+        calls.append(dict(kwargs))
+        if len(calls) == 1:
+            raise RuntimeError("Unsupported parameter: temperature is not supported")
+        return "ok"
+
+    result = call_litellm_with_param_recovery(
+        _call,
+        model="openai/custom-temp-locked",
+        call_kwargs={
+            "model": "openai/custom-temp-locked",
+            "messages": [],
+            "temperature": 0.7,
+        },
+    )
+    future_kwargs = apply_litellm_generation_params(
+        {"model": "openai/custom-temp-locked", "messages": []},
+        "openai/custom-temp-locked",
+        0.7,
+    )
+
+    assert result == "ok"
+    assert calls[0]["temperature"] == 0.7
+    assert "temperature" not in calls[1]
+    assert "temperature" not in future_kwargs
+
+
+def test_recovery_cache_is_scoped_to_api_base() -> None:
+    clear_litellm_generation_param_recovery_cache()
+    calls = []
+
+    def _call(kwargs):
+        calls.append(dict(kwargs))
+        if len(calls) == 1:
+            raise RuntimeError("Unsupported parameter: temperature is not supported")
+        return "ok"
+
+    result = call_litellm_with_param_recovery(
+        _call,
+        model="openai/shared-model",
+        call_kwargs={
+            "model": "openai/shared-model",
+            "messages": [],
+            "api_base": "https://strict.example/v1",
+            "temperature": 0.7,
+        },
+    )
+    strict_kwargs = apply_litellm_generation_params(
+        {"model": "openai/shared-model", "messages": [], "api_base": "https://strict.example/v1"},
+        "openai/shared-model",
+        0.7,
+    )
+    flexible_kwargs = apply_litellm_generation_params(
+        {"model": "openai/shared-model", "messages": [], "api_base": "https://flex.example/v1"},
+        "openai/shared-model",
+        0.7,
+    )
+
+    assert result == "ok"
+    assert "temperature" not in strict_kwargs
+    assert flexible_kwargs["temperature"] == 0.7
+
+
+def test_recovery_cache_skips_ambiguous_router_endpoints() -> None:
+    clear_litellm_generation_param_recovery_cache()
+    model_list = [
+        {
+            "model_name": "openai/shared-model",
+            "litellm_params": {
+                "model": "openai/shared-model",
+                "api_base": "https://strict.example/v1",
+            },
+        },
+        {
+            "model_name": "openai/shared-model",
+            "litellm_params": {
+                "model": "openai/shared-model",
+                "api_base": "https://flex.example/v1",
+            },
+        },
+    ]
+    calls = []
+
+    def _call(kwargs):
+        calls.append(dict(kwargs))
+        if len(calls) == 1:
+            raise RuntimeError("Unsupported parameter: temperature is not supported")
+        return "ok"
+
+    result = call_litellm_with_param_recovery(
+        _call,
+        model="openai/shared-model",
+        call_kwargs={"model": "openai/shared-model", "messages": [], "temperature": 0.7},
+        model_list=model_list,
+    )
+    future_kwargs = apply_litellm_generation_params(
+        {"model": "openai/shared-model", "messages": []},
+        "openai/shared-model",
+        0.7,
+        model_list=model_list,
+    )
+
+    assert result == "ok"
+    assert future_kwargs["temperature"] == 0.7
+
+
+def test_streaming_retry_does_not_cache_before_stream_is_consumed() -> None:
+    clear_litellm_generation_param_recovery_cache()
+    calls = []
+
+    def _broken_stream():
+        raise RuntimeError("stream failed during iteration")
+        yield  # pragma: no cover
+
+    def _call(kwargs):
+        calls.append(dict(kwargs))
+        if len(calls) == 1:
+            raise RuntimeError("Unsupported parameter: temperature is not supported")
+        return _broken_stream()
+
+    stream = call_litellm_with_param_recovery(
+        _call,
+        model="openai/stream-model",
+        call_kwargs={
+            "model": "openai/stream-model",
+            "messages": [],
+            "temperature": 0.7,
+            "stream": True,
+        },
+        cache_recovery=False,
+    )
+    try:
+        list(stream)
+    except RuntimeError:
+        pass
+    else:  # pragma: no cover
+        raise AssertionError("stream should fail during iteration")
+
+    future_kwargs = apply_litellm_generation_params(
+        {"model": "openai/stream-model", "messages": []},
+        "openai/stream-model",
+        0.7,
+    )
+
+    assert "temperature" not in calls[1]
+    assert future_kwargs["temperature"] == 0.7
--- a/tests/test_market_analyzer_generate_text.py
+++ b/tests/test_market_analyzer_generate_text.py
@@ -142,6 +142,149 @@ class TestAnalyzerGenerateText:
        assert usage == {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3}
        assert progress_updates == [3, 6]

+    def test_call_litellm_legacy_path_uses_legacy_model_list_for_param_recovery(self):
+        with patch("src.analyzer.get_config") as mock_cfg:
+            cfg = MagicMock()
+            cfg.litellm_model = "openai/gpt-4o-mini"
+            cfg.litellm_fallback_models = []
+            cfg.gemini_api_keys = []
+            cfg.anthropic_api_keys = []
+            cfg.deepseek_api_keys = []
+            cfg.openai_api_keys = ["sk-openai-legacy-a", "sk-openai-legacy-b"]
+            cfg.openai_base_url = None
+            cfg.llm_model_list = [
+                {
+                    "model_name": "__legacy_openai__",
+                    "litellm_params": {
+                        "model": "__legacy_openai__",
+                        "api_key": "sk-openai-legacy-a",
+                        "api_base": "https://legacy-a.example/v1",
+                        "extra_headers": {"x-tenant": "legacy-a"},
+                    },
+                },
+                {
+                    "model_name": "__legacy_openai__",
+                    "litellm_params": {
+                        "model": "__legacy_openai__",
+                        "api_key": "sk-openai-legacy-b",
+                        "api_base": "https://legacy-b.example/v1",
+                        "extra_headers": {"x-tenant": "legacy-b"},
+                    },
+                },
+            ]
+            cfg.llm_temperature = 0.7
+            mock_cfg.return_value = cfg
+
+            from src.analyzer import GeminiAnalyzer
+
+            analyzer = GeminiAnalyzer()
+            analyzer._config_override = cfg
+
+        captured = {}
+
+        def _fake_call_litellm_with_param_recovery(call, **kwargs):
+            captured["model_list"] = kwargs.get("model_list")
+            return SimpleNamespace(
+                choices=[SimpleNamespace(message=SimpleNamespace(content="ok"))],
+                usage=None,
+            )
+
+        with patch("src.analyzer.call_litellm_with_param_recovery", side_effect=_fake_call_litellm_with_param_recovery):
+            text, _, _ = analyzer._call_litellm("回归用例", {"max_tokens": 128, "temperature": 0.7})
+
+        assert text == "ok"
+        passed_model_list = captured.get("model_list")
+        assert passed_model_list is not None
+        assert len(passed_model_list) == 2
+        assert all(item["litellm_params"].get("model") == "openai/gpt-4o-mini" for item in passed_model_list)
+        assert [item["litellm_params"]["api_base"] for item in passed_model_list] == [
+            "https://legacy-a.example/v1",
+            "https://legacy-b.example/v1",
+        ]
+        assert [item["litellm_params"]["extra_headers"] for item in passed_model_list] == [
+            {"x-tenant": "legacy-a"},
+            {"x-tenant": "legacy-b"},
+        ]
+
+    @patch("src.analyzer.Router")
+    def test_analyzer_legacy_router_recovery_cache_is_scoped_by_api_base(self, mock_router):
+        """Analyzer legacy recovery should not leak across same model different api_base."""
+        from src.analyzer import call_litellm_with_param_recovery as real_call
+        from src.llm.generation_params import clear_litellm_generation_param_recovery_cache
+
+        clear_litellm_generation_param_recovery_cache()
+        response = SimpleNamespace(
+            choices=[SimpleNamespace(message=SimpleNamespace(content="analyzer ok"))],
+            usage=SimpleNamespace(prompt_tokens=1, completion_tokens=2, total_tokens=3),
+        )
+        strict_router = MagicMock()
+        flex_router = MagicMock()
+        strict_router.completion.side_effect = [
+            RuntimeError("Unsupported parameter: temperature is not supported"),
+            response,
+        ]
+        flex_router.completion.return_value = response
+        mock_router.side_effect = [strict_router, flex_router]
+
+        strict_cfg = SimpleNamespace(
+            litellm_model="openai/shared-model",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+            llm_temperature=0.2,
+            gemini_api_keys=[],
+            anthropic_api_keys=[],
+            openai_api_keys=["sk-strict-key-1", "sk-strict-key-2"],
+            deepseek_api_keys=[],
+            openai_base_url="https://strict.example/v1",
+        )
+        flex_cfg = SimpleNamespace(
+            litellm_model="openai/shared-model",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+            llm_temperature=0.2,
+            gemini_api_keys=[],
+            anthropic_api_keys=[],
+            openai_api_keys=["sk-flex-key-1", "sk-flex-key-2"],
+            deepseek_api_keys=[],
+            openai_base_url="https://flex.example/v1",
+        )
+
+        captured_model_lists = []
+
+        def _fake_recovery(call, **kwargs):
+            captured_model_lists.append(kwargs.get("model_list"))
+            return real_call(call, **kwargs)
+
+        import src.analyzer as analyzer_module
+        from src.analyzer import GeminiAnalyzer
+
+        with patch.object(analyzer_module, "call_litellm_with_param_recovery", side_effect=_fake_recovery):
+            GeminiAnalyzer(config=strict_cfg)._call_litellm(
+                "prompt",
+                {"max_tokens": 128, "temperature": 0.2},
+            )
+            GeminiAnalyzer(config=flex_cfg)._call_litellm(
+                "prompt",
+                {"max_tokens": 128, "temperature": 0.2},
+            )
+
+        assert len(captured_model_lists) == 2
+        strict_model_list = captured_model_lists[0]
+        flex_model_list = captured_model_lists[1]
+        assert strict_model_list is not None
+        assert flex_model_list is not None
+        assert all(
+            item.get("litellm_params", {}).get("api_base") == "https://strict.example/v1"
+            for item in strict_model_list
+        )
+        assert all(
+            item.get("litellm_params", {}).get("api_base") == "https://flex.example/v1"
+            for item in flex_model_list
+        )
+        assert strict_router.completion.call_args_list[0].kwargs["temperature"] == 0.2
+        assert "temperature" not in strict_router.completion.call_args_list[1].kwargs
+        assert flex_router.completion.call_args.kwargs["temperature"] == 0.2
+
    def test_call_litellm_stream_falls_back_to_non_stream_before_first_chunk(self):
        analyzer = self._make_analyzer()
        analyzer._config_override = SimpleNamespace(
@@ -315,6 +458,66 @@ class TestAnalyzerGenerateText:
        call_kwargs = mock_dispatch.call_args.args[1]
        assert call_kwargs["temperature"] == 0.6

+    def test_call_litellm_omits_temperature_for_gpt5_family(self):
+        analyzer = self._make_analyzer()
+        analyzer._config_override = SimpleNamespace(
+            litellm_model="openai/gpt5.5-ferr",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+        )
+        response = SimpleNamespace(
+            choices=[SimpleNamespace(message=SimpleNamespace(content="ok"))],
+            usage=SimpleNamespace(prompt_tokens=1, completion_tokens=1, total_tokens=2),
+        )
+
+        with patch.object(analyzer, "_dispatch_litellm_completion", return_value=response) as mock_dispatch:
+            text, model_used, usage = analyzer._call_litellm(
+                "prompt",
+                {"max_tokens": 128, "temperature": 0.2},
+            )
+
+        assert text == "ok"
+        assert model_used == "openai/gpt5.5-ferr"
+        assert usage == {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}
+        call_kwargs = mock_dispatch.call_args.args[1]
+        assert "temperature" not in call_kwargs
+
+    def test_call_litellm_recovers_from_temperature_default_error(self):
+        from src.llm.generation_params import clear_litellm_generation_param_recovery_cache
+
+        clear_litellm_generation_param_recovery_cache()
+        analyzer = self._make_analyzer()
+        analyzer._config_override = SimpleNamespace(
+            litellm_model="openai/custom-default-temp",
+            litellm_fallback_models=[],
+            llm_model_list=[],
+        )
+        response = SimpleNamespace(
+            choices=[SimpleNamespace(message=SimpleNamespace(content="ok"))],
+            usage=SimpleNamespace(prompt_tokens=1, completion_tokens=1, total_tokens=2),
+        )
+        calls = []
+
+        def _dispatch(model, call_kwargs, **_kwargs):
+            calls.append(dict(call_kwargs))
+            if len(calls) == 1:
+                raise RuntimeError(
+                    "temperature=0.2 is unsupported. Only the default (1.0) value is supported."
+                )
+            return response
+
+        with patch.object(analyzer, "_dispatch_litellm_completion", side_effect=_dispatch):
+            text, model_used, usage = analyzer._call_litellm(
+                "prompt",
+                {"max_tokens": 128, "temperature": 0.2},
+            )
+
+        assert text == "ok"
+        assert model_used == "openai/custom-default-temp"
+        assert usage == {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}
+        assert calls[0]["temperature"] == 0.2
+        assert calls[1]["temperature"] == 1.0
+
    def test_call_litellm_keeps_user_temperature_for_non_kimi_fallback(self):
        analyzer = self._make_analyzer()
        analyzer._config_override = SimpleNamespace(
--- a/tests/test_system_config_service.py
+++ b/tests/test_system_config_service.py
@@ -1457,6 +1457,44 @@ class SystemConfigServiceTestCase(unittest.TestCase):
        self.assertEqual(current_map["LITELLM_MODEL"], "openai/kimi-k2.6")
        self.assertEqual(current_map["LLM_TEMPERATURE"], "0.42")

+    def test_update_runtime_model_cleanup_does_not_rewrite_temperature(self) -> None:
+        self._rewrite_env(
+            "STOCK_LIST=600519,000001",
+            "LLM_CHANNELS=deepseek",
+            "LLM_DEEPSEEK_PROTOCOL=deepseek",
+            "LLM_DEEPSEEK_BASE_URL=https://api.deepseek.com",
+            "LLM_DEEPSEEK_API_KEY=sk-test-value",
+            "LLM_DEEPSEEK_MODELS=deepseek-chat,deepseek-v4-flash",
+            "LITELLM_MODEL=deepseek/deepseek-chat",
+            "AGENT_LITELLM_MODEL=deepseek/deepseek-v4-flash",
+            "LLM_TEMPERATURE=0.42",
+            "LITELLM_FALLBACK_MODELS=deepseek/deepseek-v4-flash,cohere/command-r-plus",
+            "VISION_MODEL=deepseek/deepseek-chat",
+        )
+
+        response = self.service.update(
+            config_version=self.manager.get_config_version(),
+            items=[
+                {"key": "LLM_DEEPSEEK_MODELS", "value": "deepseek-v4-flash"},
+                {"key": "LITELLM_MODEL", "value": ""},
+                {"key": "AGENT_LITELLM_MODEL", "value": ""},
+                {"key": "LITELLM_FALLBACK_MODELS", "value": "deepseek/deepseek-v4-flash"},
+                {"key": "VISION_MODEL", "value": ""},
+            ],
+            reload_now=False,
+        )
+
+        self.assertTrue(response["success"])
+        current_map = self.manager.read_config_map()
+        self.assertEqual(current_map["LLM_TEMPERATURE"], "0.42")
+        self.assertEqual(current_map["LITELLM_MODEL"], "")
+        self.assertEqual(current_map["AGENT_LITELLM_MODEL"], "")
+        self.assertEqual(current_map["VISION_MODEL"], "")
+        self.assertEqual(
+            current_map["LITELLM_FALLBACK_MODELS"],
+            "deepseek/deepseek-v4-flash",
+        )
+
    @patch("litellm.completion")
    def test_test_llm_channel_does_not_persist_normalized_kimi_temperature(self, mock_completion) -> None:
        self._rewrite_env("LLM_TEMPERATURE=0.42")
@@ -1480,6 +1518,62 @@ class SystemConfigServiceTestCase(unittest.TestCase):
        self.assertEqual(mock_completion.call_args.kwargs["temperature"], 1.0)
        self.assertEqual(self.manager.read_config_map()["LLM_TEMPERATURE"], "0.42")

+    @patch("litellm.completion")
+    def test_test_llm_channel_omits_temperature_for_gpt5_family(self, mock_completion) -> None:
+        mock_completion.return_value = type(
+            "MockResponse",
+            (),
+            {
+                "choices": [type("Choice", (), {"message": type("Message", (), {"content": "OK"})()})()],
+            },
+        )()
+
+        payload = self.service.test_llm_channel(
+            name="primary",
+            protocol="openai",
+            base_url="https://api.example.com/v1",
+            api_key="sk-test-value",
+            models=["gpt5.5-ferr"],
+        )
+
+        self.assertTrue(payload["success"])
+        self.assertEqual(payload["resolved_model"], "openai/gpt5.5-ferr")
+        self.assertNotIn("temperature", mock_completion.call_args.kwargs)
+
+    @patch("litellm.completion")
+    @patch("src.services.system_config_service.Config._load_from_env")
+    def test_test_llm_channel_recovers_from_unsupported_temperature(
+        self,
+        mock_load_config,
+        mock_completion,
+    ) -> None:
+        from src.llm.generation_params import clear_litellm_generation_param_recovery_cache
+
+        clear_litellm_generation_param_recovery_cache()
+        mock_load_config.return_value = SimpleNamespace(llm_temperature=0.42)
+        mock_completion.side_effect = [
+            RuntimeError("Unsupported parameter: temperature is not supported"),
+            type(
+                "MockResponse",
+                (),
+                {
+                    "choices": [type("Choice", (), {"message": type("Message", (), {"content": "OK"})()})()],
+                },
+            )(),
+        ]
+
+        payload = self.service.test_llm_channel(
+            name="primary",
+            protocol="openai",
+            base_url="https://api.example.com/v1",
+            api_key="sk-test-value",
+            models=["custom-temp-locked-settings"],
+        )
+
+        self.assertTrue(payload["success"])
+        self.assertEqual(mock_completion.call_args_list[0].kwargs["temperature"], 0.42)
+        self.assertNotIn("temperature", mock_completion.call_args_list[1].kwargs)
+
    @patch("litellm.completion")
    @patch("src.services.system_config_service.Config._load_from_env")
    def test_test_llm_channel_uses_runtime_temperature_for_non_kimi_models(