Harden DeepSeek agent: LiteLLM adapter, DSML/reasoning/embeddings/error fixes

- LiteLLMAdapter (subclasses OpenAIAdapter via _acreate hook): routes DeepSeek
  through LiteLLM. Opt-in AGENTIC_DEFAULT_MODEL_PROVIDER=litellm. A/B beat the
  hand-rolled adapter (0 DSML, 0 parse-fails). Defensive chunk.usage getattr,
  token-estimate usage fallback for billing, quiet litellm logs.
- DSML parser: tolerate single/multi fullwidth pipes, honor string="true/false"
  typed args (openai_adapter fallback when DeepSeek leaks tool calls as text).
- Thinking mode: capture and round-trip reasoning_content across turns.
- Embeddings: dedicated AGENTIC_EMBEDDINGS_API_KEY (DeepSeek has no embeddings);
  disable cleanly when unset to avoid per-turn 401.
- claude_format: friendly generic error messages to the chat, raw only in logs.
- acai agent max_tokens 4096->16384 (whole-file writes no longer truncate);
  system.md size-based edit policy; strict tools opt-in (off).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/adapters/litellm_adapter.py

@@ -0,0 +1,67 @@
+"""LiteLLM model adapter — spike para A/B contra el adapter OpenAI/DeepSeek nativo.
+
+Reutiliza TODO el flujo de OpenAIAdapter (procesado de chunks, conversión de
+mensajes, tools, fallback DSML) y solo cambia la llamada al modelo: en vez del
+SDK de OpenAI, enruta por LiteLLM, que trae handling específico por proveedor
+(DeepSeek incluido) y podría resolver de fábrica el DSML / reasoning_content que
+hoy parcheamos a mano.
+
+Activar con `AGENTIC_DEFAULT_MODEL_PROVIDER=litellm`. Modelo via
+`AGENTIC_LITELLM_MODEL` (p.ej. "deepseek/deepseek-v4-pro"); si vacío, deriva de
+`AGENTIC_DEFAULT_MODEL_ID`. Reusa `openai_api_key` / `openai_base_url` como
+credenciales.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import litellm
+
+from ..config import settings
+from .openai_adapter import OpenAIAdapter
+
+logger = logging.getLogger(__name__)
+
+# Que LiteLLM descarte params no soportados por el proveedor en vez de petar.
+litellm.drop_params = True
+# Silenciar el spam INFO de litellm ("LiteLLM completion() model=...").
+litellm.suppress_debug_info = True
+logging.getLogger("LiteLLM").setLevel(logging.WARNING)
+
+
+class LiteLLMAdapter(OpenAIAdapter):
+    """Enruta las llamadas por LiteLLM, reutilizando el pipeline de OpenAIAdapter."""
+
+    def __init__(
+        self,
+        model: str | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+    ) -> None:
+        # NO llamamos a super().__init__: no necesitamos el cliente AsyncOpenAI.
+        self._litellm_model = model or settings.litellm_model or self._derive_model()
+        self._api_key = api_key or settings.openai_api_key or None
+        self._api_base = base_url or settings.openai_base_url or None
+        # LiteLLM no entrega usage fiable en streaming → estimar para billing.
+        self._estimate_usage_fallback = True
+        logger.info(
+            "LiteLLMAdapter: model=%s api_base=%s",
+            self._litellm_model, self._api_base or "(default)",
+        )
+
+    @staticmethod
+    def _derive_model() -> str:
+        mid = settings.default_model_id or "deepseek-chat"
+        # Si ya trae prefijo de proveedor ("deepseek/...", "openai/..."), respetar.
+        return mid if "/" in mid else f"deepseek/{mid}"
+
+    async def _acreate(self, kwargs: dict[str, Any]):
+        kwargs = dict(kwargs)
+        kwargs["model"] = self._litellm_model
+        if self._api_key:
+            kwargs["api_key"] = self._api_key
+        if self._api_base:
+            kwargs["api_base"] = self._api_base
+        return await litellm.acompletion(**kwargs)