mirror of
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Merge remote-tracking branch 'origin/main' into sid/types-and-lints
# Conflicts: # gateway/platforms/base.py # gateway/platforms/qqbot/adapter.py # gateway/platforms/slack.py # hermes_cli/main.py # scripts/batch_runner.py # tools/skills_tool.py # uv.lock
This commit is contained in:
alt-glitch
commit
a9ed7cb3b4
117 changed files with 7791 additions and 611 deletions
232
run_agent.py
232
run_agent.py
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@ -127,7 +127,7 @@ from agent.trajectory import (
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convert_scratchpad_to_think, has_incomplete_scratchpad,
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save_trajectory as _save_trajectory_to_file,
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)
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from utils import atomic_json_write, base_url_host_matches, base_url_hostname, env_var_enabled
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from utils import atomic_json_write, base_url_host_matches, base_url_hostname, env_var_enabled, normalize_proxy_url
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@ -190,7 +190,7 @@ def _get_proxy_from_env() -> Optional[str]:
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"https_proxy", "http_proxy", "all_proxy"):
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value = os.environ.get(key, "").strip()
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if value:
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return value
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return normalize_proxy_url(value)
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return None
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@ -2358,6 +2358,13 @@ class AIAgent:
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cost reduction as direct Anthropic callers, provided their
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gateway implements the Anthropic cache_control contract
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(MiniMax, Zhipu GLM, LiteLLM's Anthropic proxy mode all do).
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Qwen / Alibaba-family models on OpenCode, OpenCode Go, and direct
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Alibaba (DashScope) also honour Anthropic-style ``cache_control``
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markers on OpenAI-wire chat completions. Upstream pi-mono #3392 /
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pi #3393 documented this for opencode-go Qwen. Without markers
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these providers serve zero cache hits, re-billing the full prompt
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on every turn.
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"""
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eff_provider = (provider if provider is not None else self.provider) or ""
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eff_base_url = base_url if base_url is not None else (self.base_url or "")
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@ -2365,7 +2372,9 @@ class AIAgent:
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eff_model = (model if model is not None else self.model) or ""
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base_lower = eff_base_url.lower()
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is_claude = "claude" in eff_model.lower()
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model_lower = eff_model.lower()
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provider_lower = eff_provider.lower()
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is_claude = "claude" in model_lower
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is_openrouter = base_url_host_matches(eff_base_url, "openrouter.ai")
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is_anthropic_wire = eff_api_mode == "anthropic_messages"
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is_native_anthropic = (
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@ -2380,6 +2389,22 @@ class AIAgent:
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if is_anthropic_wire and is_claude:
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# Third-party Anthropic-compatible gateway.
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return True, True
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# Qwen/Alibaba on OpenCode (Zen/Go) and native DashScope: OpenAI-wire
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# transport that accepts Anthropic-style cache_control markers and
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# rewards them with real cache hits. Without this branch
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# qwen3.6-plus on opencode-go reports 0% cached tokens and burns
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# through the subscription on every turn.
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model_is_qwen = "qwen" in model_lower
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provider_is_alibaba_family = provider_lower in {
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"opencode", "opencode-zen", "opencode-go", "alibaba",
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}
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if provider_is_alibaba_family and model_is_qwen:
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# Envelope layout (native_anthropic=False): markers on inner
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# content parts, not top-level tool messages. Matches
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# pi-mono's "alibaba" cacheControlFormat.
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return True, False
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return False, False
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@staticmethod
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@ -6126,8 +6151,9 @@ class AIAgent:
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fb_base_url_hint = (fb.get("base_url") or "").strip() or None
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fb_api_key_hint = (fb.get("api_key") or "").strip() or None
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# For Ollama Cloud endpoints, pull OLLAMA_API_KEY from env
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# when no explicit key is in the fallback config.
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if fb_base_url_hint and "ollama.com" in fb_base_url_hint.lower() and not fb_api_key_hint:
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# when no explicit key is in the fallback config. Host match
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# (not substring) — see GHSA-76xc-57q6-vm5m.
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if fb_base_url_hint and base_url_host_matches(fb_base_url_hint, "ollama.com") and not fb_api_key_hint:
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fb_api_key_hint = os.getenv("OLLAMA_API_KEY") or None
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fb_client, _resolved_fb_model = resolve_provider_client(
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fb_provider, model=fb_model, raw_codex=True,
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@ -6548,6 +6574,15 @@ class AIAgent:
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return suffix
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return "[A multimodal message was converted to text for Anthropic compatibility.]"
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def _get_anthropic_transport(self):
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"""Return the cached AnthropicTransport instance (lazy singleton)."""
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t = getattr(self, "_anthropic_transport", None)
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if t is None:
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from agent.transports import get_transport
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t = get_transport("anthropic_messages")
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self._anthropic_transport = t
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return t
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def _prepare_anthropic_messages_for_api(self, api_messages: list) -> list:
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if not any(
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isinstance(msg, dict) and self._content_has_image_parts(msg.get("content"))
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@ -6664,20 +6699,14 @@ class AIAgent:
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def _build_api_kwargs(self, api_messages: list) -> dict:
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"""Build the keyword arguments dict for the active API mode."""
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if self.api_mode == "anthropic_messages":
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from agent.anthropic_adapter import build_anthropic_kwargs
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_transport = self._get_anthropic_transport()
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anthropic_messages = self._prepare_anthropic_messages_for_api(api_messages)
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# Pass context_length (total input+output window) so the adapter can
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# clamp max_tokens (output cap) when the user configured a smaller
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# context window than the model's native output limit.
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ctx_len = getattr(self, "context_compressor", None)
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ctx_len = ctx_len.context_length if ctx_len else None
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# _ephemeral_max_output_tokens is set for one call when the API
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# returns "max_tokens too large given prompt" — it caps output to
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# the available window space without touching context_length.
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ephemeral_out = getattr(self, "_ephemeral_max_output_tokens", None)
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if ephemeral_out is not None:
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self._ephemeral_max_output_tokens = None # consume immediately
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return build_anthropic_kwargs(
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return _transport.build_kwargs(
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model=self.model,
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messages=anthropic_messages,
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tools=self.tools,
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@ -6909,6 +6938,34 @@ class AIAgent:
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# (the documented max output for qwen3-coder models) so the
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# model has adequate output budget for tool calls.
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api_kwargs.update(self._max_tokens_param(65536))
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elif (
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base_url_host_matches(self.base_url, "api.kimi.com")
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or base_url_host_matches(self.base_url, "moonshot.ai")
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or base_url_host_matches(self.base_url, "moonshot.cn")
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):
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# Kimi/Moonshot defaults to a low max_tokens when omitted.
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# Reasoning tokens share the output budget — without an explicit
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# value the model can exhaust it on thinking alone, causing
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# "Response truncated due to output length limit". 32000 matches
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# Kimi CLI's default (see MoonshotAI/kimi-cli kimi.py generate()).
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api_kwargs.update(self._max_tokens_param(32000))
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# Kimi requires reasoning_effort as a top-level chat completions
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# parameter (not inside extra_body). Mirror Kimi CLI's
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# with_generation_kwargs(reasoning_effort=...) / with_thinking():
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# when thinking is disabled, Kimi CLI omits reasoning_effort
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# entirely (maps to None).
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_kimi_thinking_off = bool(
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self.reasoning_config
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and isinstance(self.reasoning_config, dict)
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and self.reasoning_config.get("enabled") is False
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)
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if not _kimi_thinking_off:
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_kimi_effort = "medium"
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if self.reasoning_config and isinstance(self.reasoning_config, dict):
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_e = (self.reasoning_config.get("effort") or "").strip().lower()
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if _e in ("low", "medium", "high"):
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_kimi_effort = _e
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api_kwargs["reasoning_effort"] = _kimi_effort
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elif (self._is_openrouter_url() or "nousresearch" in self._base_url_lower) and "claude" in (self.model or "").lower():
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# OpenRouter and Nous Portal translate requests to Anthropic's
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# Messages API, which requires max_tokens as a mandatory field.
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@ -6940,6 +6997,24 @@ class AIAgent:
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extra_body["provider"] = provider_preferences
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_is_nous = "nousresearch" in self._base_url_lower
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# Kimi/Moonshot API uses extra_body.thinking (separate from the
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# top-level reasoning_effort) to enable/disable reasoning mode.
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# Mirror Kimi CLI's with_thinking() behavior exactly — see
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# MoonshotAI/kimi-cli packages/kosong/src/kosong/chat_provider/kimi.py
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_is_kimi = (
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base_url_host_matches(self.base_url, "api.kimi.com")
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or base_url_host_matches(self.base_url, "moonshot.ai")
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or base_url_host_matches(self.base_url, "moonshot.cn")
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)
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if _is_kimi:
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_kimi_thinking_enabled = True
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if self.reasoning_config and isinstance(self.reasoning_config, dict):
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if self.reasoning_config.get("enabled") is False:
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_kimi_thinking_enabled = False
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extra_body["thinking"] = {
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"type": "enabled" if _kimi_thinking_enabled else "disabled",
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}
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if self._supports_reasoning_extra_body():
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if _is_github_models:
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github_reasoning = self._github_models_reasoning_extra_body()
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@ -7362,9 +7437,9 @@ class AIAgent:
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codex_kwargs["max_output_tokens"] = 5120
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response = self._run_codex_stream(codex_kwargs)
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elif not _aux_available and self.api_mode == "anthropic_messages":
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# Native Anthropic — use the Anthropic client directly
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from agent.anthropic_adapter import build_anthropic_kwargs as _build_ant_kwargs
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ant_kwargs = _build_ant_kwargs(
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# Native Anthropic — use the transport for kwargs
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_tflush = self._get_anthropic_transport()
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ant_kwargs = _tflush.build_kwargs(
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model=self.model, messages=api_messages,
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tools=[memory_tool_def], max_tokens=5120,
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reasoning_config=None,
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@ -7392,10 +7467,15 @@ class AIAgent:
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if assistant_msg and assistant_msg.tool_calls:
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tool_calls = assistant_msg.tool_calls
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elif self.api_mode == "anthropic_messages" and not _aux_available:
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from agent.anthropic_adapter import normalize_anthropic_response as _nar_flush
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_flush_msg, _ = _nar_flush(response, strip_tool_prefix=self._is_anthropic_oauth)
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if _flush_msg and _flush_msg.tool_calls:
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tool_calls = _flush_msg.tool_calls
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_tfn = self._get_anthropic_transport()
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_flush_nr = _tfn.normalize_response(response, strip_tool_prefix=self._is_anthropic_oauth)
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if _flush_nr and _flush_nr.tool_calls:
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tool_calls = [
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SimpleNamespace(
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id=tc.id, type="function",
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function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
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) for tc in _flush_nr.tool_calls
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]
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elif hasattr(response, "choices") and response.choices:
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assistant_message = response.choices[0].message
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if assistant_message.tool_calls:
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@ -8455,14 +8535,14 @@ class AIAgent:
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summary_kwargs["extra_body"] = summary_extra_body
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if self.api_mode == "anthropic_messages":
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from agent.anthropic_adapter import build_anthropic_kwargs as _bak, normalize_anthropic_response as _nar
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_ant_kw = _bak(model=self.model, messages=api_messages, tools=None,
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_tsum = self._get_anthropic_transport()
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_ant_kw = _tsum.build_kwargs(model=self.model, messages=api_messages, tools=None,
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max_tokens=self.max_tokens, reasoning_config=self.reasoning_config,
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is_oauth=self._is_anthropic_oauth,
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preserve_dots=self._anthropic_preserve_dots())
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summary_response = self._anthropic_messages_create(_ant_kw)
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_msg, _ = _nar(summary_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_msg.content or "").strip()
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_sum_nr = _tsum.normalize_response(summary_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_sum_nr.content or "").strip()
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else:
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summary_response = self._ensure_primary_openai_client(reason="iteration_limit_summary").chat.completions.create(**summary_kwargs)
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|
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@ -8487,14 +8567,14 @@ class AIAgent:
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retry_msg, _ = self._normalize_codex_response(retry_response)
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final_response = (retry_msg.content or "").strip() if retry_msg else ""
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elif self.api_mode == "anthropic_messages":
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from agent.anthropic_adapter import build_anthropic_kwargs as _bak2, normalize_anthropic_response as _nar2
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_ant_kw2 = _bak2(model=self.model, messages=api_messages, tools=None,
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_tretry = self._get_anthropic_transport()
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_ant_kw2 = _tretry.build_kwargs(model=self.model, messages=api_messages, tools=None,
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is_oauth=self._is_anthropic_oauth,
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max_tokens=self.max_tokens, reasoning_config=self.reasoning_config,
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preserve_dots=self._anthropic_preserve_dots())
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retry_response = self._anthropic_messages_create(_ant_kw2)
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_retry_msg, _ = _nar2(retry_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_retry_msg.content or "").strip()
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_retry_nr = _tretry.normalize_response(retry_response, strip_tool_prefix=self._is_anthropic_oauth)
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final_response = (_retry_nr.content or "").strip()
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else:
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summary_kwargs = {
|
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"model": self.model,
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|
|
@ -9363,16 +9443,13 @@ class AIAgent:
|
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response_invalid = True
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error_details.append("response.output is empty")
|
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elif self.api_mode == "anthropic_messages":
|
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content_blocks = getattr(response, "content", None) if response is not None else None
|
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if response is None:
|
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_tv = self._get_anthropic_transport()
|
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if not _tv.validate_response(response):
|
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response_invalid = True
|
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error_details.append("response is None")
|
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elif not isinstance(content_blocks, list):
|
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response_invalid = True
|
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error_details.append("response.content is not a list")
|
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elif not content_blocks:
|
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response_invalid = True
|
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error_details.append("response.content is empty")
|
||||
if response is None:
|
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error_details.append("response is None")
|
||||
else:
|
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error_details.append("response.content invalid (not a non-empty list)")
|
||||
else:
|
||||
if response is None or not hasattr(response, 'choices') or response.choices is None or not response.choices:
|
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response_invalid = True
|
||||
|
|
@ -9533,8 +9610,8 @@ class AIAgent:
|
|||
else:
|
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finish_reason = "stop"
|
||||
elif self.api_mode == "anthropic_messages":
|
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stop_reason_map = {"end_turn": "stop", "tool_use": "tool_calls", "max_tokens": "length", "stop_sequence": "stop"}
|
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finish_reason = stop_reason_map.get(response.stop_reason, "stop")
|
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_tfr = self._get_anthropic_transport()
|
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finish_reason = _tfr.map_finish_reason(response.stop_reason)
|
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else:
|
||||
finish_reason = response.choices[0].finish_reason
|
||||
assistant_message = response.choices[0].message
|
||||
|
|
@ -9563,10 +9640,24 @@ class AIAgent:
|
|||
if self.api_mode in ("chat_completions", "bedrock_converse"):
|
||||
_trunc_msg = response.choices[0].message if (hasattr(response, "choices") and response.choices) else None
|
||||
elif self.api_mode == "anthropic_messages":
|
||||
from agent.anthropic_adapter import normalize_anthropic_response
|
||||
_trunc_msg, _ = normalize_anthropic_response(
|
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_trunc_nr = self._get_anthropic_transport().normalize_response(
|
||||
response, strip_tool_prefix=self._is_anthropic_oauth
|
||||
)
|
||||
_trunc_msg = SimpleNamespace(
|
||||
content=_trunc_nr.content,
|
||||
tool_calls=[
|
||||
SimpleNamespace(
|
||||
id=tc.id, type="function",
|
||||
function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
|
||||
) for tc in (_trunc_nr.tool_calls or [])
|
||||
] or None,
|
||||
reasoning=_trunc_nr.reasoning,
|
||||
reasoning_content=None,
|
||||
reasoning_details=(
|
||||
_trunc_nr.provider_data.get("reasoning_details")
|
||||
if _trunc_nr.provider_data else None
|
||||
),
|
||||
)
|
||||
|
||||
_trunc_content = getattr(_trunc_msg, "content", None) if _trunc_msg else None
|
||||
_trunc_has_tool_calls = bool(getattr(_trunc_msg, "tool_calls", None)) if _trunc_msg else False
|
||||
|
|
@ -9822,21 +9913,27 @@ class AIAgent:
|
|||
if self.verbose_logging:
|
||||
logging.debug(f"Token usage: prompt={usage_dict['prompt_tokens']:,}, completion={usage_dict['completion_tokens']:,}, total={usage_dict['total_tokens']:,}")
|
||||
|
||||
# Log cache hit stats when prompt caching is active
|
||||
if self._use_prompt_caching:
|
||||
if self.api_mode == "anthropic_messages":
|
||||
# Anthropic uses cache_read_input_tokens / cache_creation_input_tokens
|
||||
cached = getattr(response.usage, 'cache_read_input_tokens', 0) or 0
|
||||
written = getattr(response.usage, 'cache_creation_input_tokens', 0) or 0
|
||||
else:
|
||||
# OpenRouter uses prompt_tokens_details.cached_tokens
|
||||
details = getattr(response.usage, 'prompt_tokens_details', None)
|
||||
cached = getattr(details, 'cached_tokens', 0) or 0 if details else 0
|
||||
written = getattr(details, 'cache_write_tokens', 0) or 0 if details else 0
|
||||
prompt = usage_dict["prompt_tokens"]
|
||||
# Surface cache hit stats for any provider that reports
|
||||
# them — not just those where we inject cache_control
|
||||
# markers. OpenAI/Kimi/DeepSeek/Qwen all do automatic
|
||||
# server-side prefix caching and return
|
||||
# ``prompt_tokens_details.cached_tokens``; users
|
||||
# previously could not see their cache % because this
|
||||
# line was gated on ``_use_prompt_caching``, which is
|
||||
# only True for Anthropic-style marker injection.
|
||||
# ``canonical_usage`` is already normalised from all
|
||||
# three API shapes (Anthropic / Codex / OpenAI-chat)
|
||||
# so we can rely on its values directly.
|
||||
cached = canonical_usage.cache_read_tokens
|
||||
written = canonical_usage.cache_write_tokens
|
||||
prompt = usage_dict["prompt_tokens"]
|
||||
if (cached or written) and not self.quiet_mode:
|
||||
hit_pct = (cached / prompt * 100) if prompt > 0 else 0
|
||||
if not self.quiet_mode:
|
||||
self._vprint(f"{self.log_prefix} 💾 Cache: {cached:,}/{prompt:,} tokens ({hit_pct:.0f}% hit, {written:,} written)")
|
||||
self._vprint(
|
||||
f"{self.log_prefix} 💾 Cache: "
|
||||
f"{cached:,}/{prompt:,} tokens "
|
||||
f"({hit_pct:.0f}% hit, {written:,} written)"
|
||||
)
|
||||
|
||||
has_retried_429 = False # Reset on success
|
||||
# Clear Nous rate limit state on successful request —
|
||||
|
|
@ -10772,10 +10869,31 @@ class AIAgent:
|
|||
if self.api_mode == "codex_responses":
|
||||
assistant_message, finish_reason = self._normalize_codex_response(response)
|
||||
elif self.api_mode == "anthropic_messages":
|
||||
from agent.anthropic_adapter import normalize_anthropic_response
|
||||
assistant_message, finish_reason = normalize_anthropic_response(
|
||||
_transport = self._get_anthropic_transport()
|
||||
_nr = _transport.normalize_response(
|
||||
response, strip_tool_prefix=self._is_anthropic_oauth
|
||||
)
|
||||
# Back-compat shim: downstream code expects SimpleNamespace with
|
||||
# .content, .tool_calls, .reasoning, .reasoning_content,
|
||||
# .reasoning_details attributes.
|
||||
assistant_message = SimpleNamespace(
|
||||
content=_nr.content,
|
||||
tool_calls=[
|
||||
SimpleNamespace(
|
||||
id=tc.id,
|
||||
type="function",
|
||||
function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
|
||||
)
|
||||
for tc in (_nr.tool_calls or [])
|
||||
] or None,
|
||||
reasoning=_nr.reasoning,
|
||||
reasoning_content=None,
|
||||
reasoning_details=(
|
||||
_nr.provider_data.get("reasoning_details")
|
||||
if _nr.provider_data else None
|
||||
),
|
||||
)
|
||||
finish_reason = _nr.finish_reason
|
||||
else:
|
||||
assistant_message = response.choices[0].message
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue