feat(ai_client): Add Grok (xAI) OpenAI-compatible provider

src/ai_client.py

@@ -136,6 +136,16 @@ _qwen_history: list[dict[str, Any]] = []
 _qwen_history_lock: threading.Lock = threading.Lock()
 _qwen_region: str = "china"
 
+_grok_client: Any = None
+_grok_history: list[dict[str, Any]] = []
+_grok_history_lock: threading.Lock = threading.Lock()
+
+_llama_client: Any = None
+_llama_history: list[dict[str, Any]] = []
+_llama_history_lock: threading.Lock = threading.Lock()
+_llama_base_url: str = "http://localhost:11434/v1"
+_llama_api_key: str = "ollama"
+
 _send_lock: threading.Lock = threading.Lock()
 
 _BIAS_ENGINE = ToolBiasEngine()
@@ -522,6 +532,14 @@ def reset_session() -> None:
  _qwen_client    = None
  with _qwen_history_lock:
   _qwen_history       = []
+ _grok_client = None
+ with _grok_history_lock:
+  _grok_history = []
+ _llama_client = None
+ with _llama_history_lock:
+  _llama_history = []
+ _llama_base_url = "http://localhost:11434/v1"
+ _llama_api_key = "ollama"
  _CACHED_ANTHROPIC_TOOLS = None
  _CACHED_DEEPSEEK_TOOLS  = None
  file_cache.reset_client()
@@ -537,6 +555,8 @@ def list_models(provider: str) -> list[str]:
  elif provider == "gemini_cli": return _list_gemini_cli_models()
  elif provider == "minimax":    return _list_minimax_models(creds["minimax"]["api_key"])
  elif provider == "qwen":      return _list_qwen_models()
+ elif provider == "grok":     return _list_grok_models()
+ elif provider == "llama":    return _list_llama_models()
  return []
 
 #endregion: Comms Log
@@ -2150,6 +2170,58 @@ def _ensure_minimax_client() -> None:
    raise ValueError("MiniMax API key not found in credentials.toml")
   _minimax_client = OpenAI(api_key=api_key, base_url="https://api.minimax.chat/v1")
 
+def _ensure_grok_client() -> Any:
+ global _grok_client
+ if _grok_client is None:
+  openai = _require_warmed("openai")
+  creds = _load_credentials()
+  api_key = creds.get("grok", {}).get("api_key")
+  if not api_key:
+   raise ValueError("Grok API key not found in credentials.toml")
+  _grok_client = openai.OpenAI(api_key=api_key, base_url="https://api.x.ai/v1")
+ return _grok_client
+
+def _send_grok(md_content: str, user_message: str, base_dir: str,
+ file_items: list[dict[str, Any]] | None = None,
+ discussion_history: str = "",
+ stream: bool = False,
+ pre_tool_callback: Optional[Callable[[str, str, Optional[Callable[[str], str]]], Optional[str]]] = None,
+ qa_callback: Optional[Callable[[str], str]] = None,
+ stream_callback: Optional[Callable[[str], None]] = None,
+ patch_callback: Optional[Callable[[str, str], Optional[str]]] = None) -> str:
+ client = _ensure_grok_client()
+ from src.openai_compatible import OpenAICompatibleRequest, send_openai_compatible
+ from src.vendor_capabilities import get_capabilities
+ with _grok_history_lock:
+  user_content = user_message
+  if file_items:
+   for fi in file_items:
+    if fi.get("is_image") and fi.get("base64_data"):
+     user_content = f"[IMAGE: {fi.get('path', 'attachment')}]\n{user_content}"
+  if discussion_history and not _grok_history:
+   _grok_history.append({"role": "user", "content": f"[DISCUSSION HISTORY]\n\n{discussion_history}\n\n---\n\n{user_message}"})
+  else:
+   _grok_history.append({"role": "user", "content": user_content})
+  messages = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
+  messages.extend(_grok_history)
+ request = OpenAICompatibleRequest(
+  messages=messages,
+  model=_model,
+  temperature=_temperature,
+  top_p=_top_p,
+  max_tokens=_max_tokens,
+  stream=stream,
+  stream_callback=stream_callback,
+ )
+ caps = get_capabilities("grok", _model)
+ response = send_openai_compatible(client, request, capabilities=caps)
+ _grok_history.append({"role": "assistant", "content": response.text})
+ return response.text
+
+def _list_grok_models() -> list[str]:
+ from src.vendor_capabilities import list_models_for_vendor
+ return list_models_for_vendor("grok")
+
 def _send_minimax(md_content: str, user_message: str, base_dir: str,
  file_items: list[dict[str, Any]] | None = None,
  discussion_history: str = "",
@@ -2487,6 +2559,73 @@ def _send_qwen(md_content: str, user_message: str, base_dir: str,
 
 #endregion: Qwen Provider
 
+def _ensure_llama_client() -> Any:
+ global _llama_client, _llama_base_url, _llama_api_key
+ if _llama_client is None:
+  openai = _require_warmed("openai")
+  creds = _load_credentials()
+  configured_url = creds.get("llama", {}).get("base_url")
+  configured_key = creds.get("llama", {}).get("api_key")
+  if configured_url:
+   _llama_base_url = configured_url
+  if configured_key is not None:
+   _llama_api_key = configured_key or "ollama"
+  _llama_client = openai.OpenAI(api_key=_llama_api_key, base_url=_llama_base_url)
+ return _llama_client
+
+def _send_llama(md_content: str, user_message: str, base_dir: str,
+ file_items: list[dict[str, Any]] | None = None,
+ discussion_history: str = "",
+ stream: bool = False,
+ pre_tool_callback: Optional[Callable[[str, str, Optional[Callable[[str], str]]], Optional[str]]] = None,
+ qa_callback: Optional[Callable[[str], str]] = None,
+ stream_callback: Optional[Callable[[str], None]] = None,
+ patch_callback: Optional[Callable[[str, str], Optional[str]]] = None) -> str:
+ client = _ensure_llama_client()
+ from src.openai_compatible import OpenAICompatibleRequest, send_openai_compatible
+ from src.vendor_capabilities import get_capabilities
+ with _llama_history_lock:
+  user_content = user_message
+  if file_items:
+   for fi in file_items:
+    if fi.get("is_image") and fi.get("base64_data"):
+     user_content = f"[IMAGE: {fi.get('path', 'attachment')}]\n{user_content}"
+  if discussion_history and not _llama_history:
+   _llama_history.append({"role": "user", "content": f"[DISCUSSION HISTORY]\n\n{discussion_history}\n\n---\n\n{user_message}"})
+  else:
+   _llama_history.append({"role": "user", "content": user_content})
+  messages = [{"role": "system", "content": f"{_get_combined_system_prompt()}\n\n<context>\n{md_content}\n</context>"}]
+  messages.extend(_llama_history)
+ request = OpenAICompatibleRequest(
+  messages=messages,
+  model=_model,
+  temperature=_temperature,
+  top_p=_top_p,
+  max_tokens=_max_tokens,
+  stream=stream,
+  stream_callback=stream_callback,
+ )
+ caps = get_capabilities("llama", _model)
+ response = send_openai_compatible(client, request, capabilities=caps)
+ _llama_history.append({"role": "assistant", "content": response.text})
+ return response.text
+
+def _list_llama_models() -> list[str]:
+ from src.vendor_capabilities import list_models_for_vendor
+ return list_models_for_vendor("llama")
+
+def _get_llama_cost_tracking() -> bool:
+ if "localhost" in _llama_base_url or "127.0.0.1" in _llama_base_url:
+  return False
+ from src.vendor_capabilities import get_capabilities
+ try:
+  caps = get_capabilities("llama", _model)
+  return caps.cost_tracking
+ except KeyError:
+  return True
+
+#endregion: Llama Provider
+
 #region: Tier 4 Analysis
 
 def run_tier4_analysis(stderr: str) -> str: