refactor(restructure): git mv all source files into hermes_agent/ package

Pure file moves, zero content changes. Every file in agent/, tools/,
hermes_cli/, gateway/, acp_adapter/, cron/, and plugins/ moves into
hermes_agent/. Top-level modules (run_agent.py, cli.py, etc.) move to
their new homes per the restructure manifest.

Git sees 100% similarity on all moves.

Part of #14182, #14183

hermes_agent/providers/gemini_cloudcode_adapter.py

@@ -0,0 +1,905 @@
+"""OpenAI-compatible facade that talks to Google's Cloud Code Assist backend.
+
+This adapter lets Hermes use the ``google-gemini-cli`` provider as if it were
+a standard OpenAI-shaped chat completion endpoint, while the underlying HTTP
+traffic goes to ``cloudcode-pa.googleapis.com/v1internal:{generateContent,
+streamGenerateContent}`` with a Bearer access token obtained via OAuth PKCE.
+
+Architecture
+------------
+- ``GeminiCloudCodeClient`` exposes ``.chat.completions.create(**kwargs)``
+  mirroring the subset of the OpenAI SDK that ``run_agent.py`` uses.
+- Incoming OpenAI ``messages[]`` / ``tools[]`` / ``tool_choice`` are translated
+  to Gemini's native ``contents[]`` / ``tools[].functionDeclarations`` /
+  ``toolConfig`` / ``systemInstruction`` shape.
+- The request body is wrapped ``{project, model, user_prompt_id, request}``
+  per Code Assist API expectations.
+- Responses (``candidates[].content.parts[]``) are converted back to
+  OpenAI ``choices[0].message`` shape with ``content`` + ``tool_calls``.
+- Streaming uses SSE (``?alt=sse``) and yields OpenAI-shaped delta chunks.
+
+Attribution
+-----------
+Translation semantics follow jenslys/opencode-gemini-auth (MIT) and the public
+Gemini API docs. Request envelope shape
+(``{project, model, user_prompt_id, request}``) is documented nowhere; it is
+reverse-engineered from the opencode-gemini-auth and clawdbot implementations.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import time
+import uuid
+from types import SimpleNamespace
+from typing import Any, Dict, Iterator, List, Optional
+
+import httpx
+
+from agent import google_oauth
+from agent.gemini_schema import sanitize_gemini_tool_parameters
+from agent.google_code_assist import (
+    CODE_ASSIST_ENDPOINT,
+    FREE_TIER_ID,
+    CodeAssistError,
+    ProjectContext,
+    resolve_project_context,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# =============================================================================
+# Request translation: OpenAI → Gemini
+# =============================================================================
+
+_ROLE_MAP_OPENAI_TO_GEMINI = {
+    "user": "user",
+    "assistant": "model",
+    "system": "user",   # handled separately via systemInstruction
+    "tool": "user",     # functionResponse is wrapped in a user-role turn
+    "function": "user",
+}
+
+
+def _coerce_content_to_text(content: Any) -> str:
+    """OpenAI content may be str or a list of parts; reduce to plain text."""
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        pieces: List[str] = []
+        for p in content:
+            if isinstance(p, str):
+                pieces.append(p)
+            elif isinstance(p, dict):
+                if p.get("type") == "text" and isinstance(p.get("text"), str):
+                    pieces.append(p["text"])
+                # Multimodal (image_url, etc.) — stub for now; log and skip
+                elif p.get("type") in ("image_url", "input_audio"):
+                    logger.debug("Dropping multimodal part (not yet supported): %s", p.get("type"))
+        return "\n".join(pieces)
+    return str(content)
+
+
+def _translate_tool_call_to_gemini(tool_call: Dict[str, Any]) -> Dict[str, Any]:
+    """OpenAI tool_call -> Gemini functionCall part."""
+    fn = tool_call.get("function") or {}
+    args_raw = fn.get("arguments", "")
+    try:
+        args = json.loads(args_raw) if isinstance(args_raw, str) and args_raw else {}
+    except json.JSONDecodeError:
+        args = {"_raw": args_raw}
+    if not isinstance(args, dict):
+        args = {"_value": args}
+    return {
+        "functionCall": {
+            "name": fn.get("name") or "",
+            "args": args,
+        },
+        # Sentinel signature — matches opencode-gemini-auth's approach.
+        # Without this, Code Assist rejects function calls that originated
+        # outside its own chain.
+        "thoughtSignature": "skip_thought_signature_validator",
+    }
+
+
+def _translate_tool_result_to_gemini(message: Dict[str, Any]) -> Dict[str, Any]:
+    """OpenAI tool-role message -> Gemini functionResponse part.
+
+    The function name isn't in the OpenAI tool message directly; it must be
+    passed via the assistant message that issued the call. For simplicity we
+    look up ``name`` on the message (OpenAI SDK copies it there) or on the
+    ``tool_call_id`` cross-reference.
+    """
+    name = str(message.get("name") or message.get("tool_call_id") or "tool")
+    content = _coerce_content_to_text(message.get("content"))
+    # Gemini expects the response as a dict under `response`. We wrap plain
+    # text in {"output": "..."}.
+    try:
+        parsed = json.loads(content) if content.strip().startswith(("{", "[")) else None
+    except json.JSONDecodeError:
+        parsed = None
+    response = parsed if isinstance(parsed, dict) else {"output": content}
+    return {
+        "functionResponse": {
+            "name": name,
+            "response": response,
+        },
+    }
+
+
+def _build_gemini_contents(
+    messages: List[Dict[str, Any]],
+) -> tuple[List[Dict[str, Any]], Optional[Dict[str, Any]]]:
+    """Convert OpenAI messages[] to Gemini contents[] + systemInstruction."""
+    system_text_parts: List[str] = []
+    contents: List[Dict[str, Any]] = []
+
+    for msg in messages:
+        if not isinstance(msg, dict):
+            continue
+        role = str(msg.get("role") or "user")
+
+        if role == "system":
+            system_text_parts.append(_coerce_content_to_text(msg.get("content")))
+            continue
+
+        # Tool result message — emit a user-role turn with functionResponse
+        if role == "tool" or role == "function":
+            contents.append({
+                "role": "user",
+                "parts": [_translate_tool_result_to_gemini(msg)],
+            })
+            continue
+
+        gemini_role = _ROLE_MAP_OPENAI_TO_GEMINI.get(role, "user")
+        parts: List[Dict[str, Any]] = []
+
+        text = _coerce_content_to_text(msg.get("content"))
+        if text:
+            parts.append({"text": text})
+
+        # Assistant messages can carry tool_calls
+        tool_calls = msg.get("tool_calls") or []
+        if isinstance(tool_calls, list):
+            for tc in tool_calls:
+                if isinstance(tc, dict):
+                    parts.append(_translate_tool_call_to_gemini(tc))
+
+        if not parts:
+            # Gemini rejects empty parts; skip the turn entirely
+            continue
+
+        contents.append({"role": gemini_role, "parts": parts})
+
+    system_instruction: Optional[Dict[str, Any]] = None
+    joined_system = "\n".join(p for p in system_text_parts if p).strip()
+    if joined_system:
+        system_instruction = {
+            "role": "system",
+            "parts": [{"text": joined_system}],
+        }
+
+    return contents, system_instruction
+
+
+def _translate_tools_to_gemini(tools: Any) -> List[Dict[str, Any]]:
+    """OpenAI tools[] -> Gemini tools[].functionDeclarations[]."""
+    if not isinstance(tools, list) or not tools:
+        return []
+    declarations: List[Dict[str, Any]] = []
+    for t in tools:
+        if not isinstance(t, dict):
+            continue
+        fn = t.get("function") or {}
+        if not isinstance(fn, dict):
+            continue
+        name = fn.get("name")
+        if not name:
+            continue
+        decl = {"name": str(name)}
+        if fn.get("description"):
+            decl["description"] = str(fn["description"])
+        params = fn.get("parameters")
+        if isinstance(params, dict):
+            decl["parameters"] = sanitize_gemini_tool_parameters(params)
+        declarations.append(decl)
+    if not declarations:
+        return []
+    return [{"functionDeclarations": declarations}]
+
+
+def _translate_tool_choice_to_gemini(tool_choice: Any) -> Optional[Dict[str, Any]]:
+    """OpenAI tool_choice -> Gemini toolConfig.functionCallingConfig."""
+    if tool_choice is None:
+        return None
+    if isinstance(tool_choice, str):
+        if tool_choice == "auto":
+            return {"functionCallingConfig": {"mode": "AUTO"}}
+        if tool_choice == "required":
+            return {"functionCallingConfig": {"mode": "ANY"}}
+        if tool_choice == "none":
+            return {"functionCallingConfig": {"mode": "NONE"}}
+    if isinstance(tool_choice, dict):
+        fn = tool_choice.get("function") or {}
+        name = fn.get("name")
+        if name:
+            return {
+                "functionCallingConfig": {
+                    "mode": "ANY",
+                    "allowedFunctionNames": [str(name)],
+                },
+            }
+    return None
+
+
+def _normalize_thinking_config(config: Any) -> Optional[Dict[str, Any]]:
+    """Accept thinkingBudget / thinkingLevel / includeThoughts (+ snake_case)."""
+    if not isinstance(config, dict) or not config:
+        return None
+    budget = config.get("thinkingBudget", config.get("thinking_budget"))
+    level = config.get("thinkingLevel", config.get("thinking_level"))
+    include = config.get("includeThoughts", config.get("include_thoughts"))
+    normalized: Dict[str, Any] = {}
+    if isinstance(budget, (int, float)):
+        normalized["thinkingBudget"] = int(budget)
+    if isinstance(level, str) and level.strip():
+        normalized["thinkingLevel"] = level.strip().lower()
+    if isinstance(include, bool):
+        normalized["includeThoughts"] = include
+    return normalized or None
+
+
+def build_gemini_request(
+    *,
+    messages: List[Dict[str, Any]],
+    tools: Any = None,
+    tool_choice: Any = None,
+    temperature: Optional[float] = None,
+    max_tokens: Optional[int] = None,
+    top_p: Optional[float] = None,
+    stop: Any = None,
+    thinking_config: Any = None,
+) -> Dict[str, Any]:
+    """Build the inner Gemini request body (goes inside ``request`` wrapper)."""
+    contents, system_instruction = _build_gemini_contents(messages)
+
+    body: Dict[str, Any] = {"contents": contents}
+    if system_instruction is not None:
+        body["systemInstruction"] = system_instruction
+
+    gemini_tools = _translate_tools_to_gemini(tools)
+    if gemini_tools:
+        body["tools"] = gemini_tools
+    tool_cfg = _translate_tool_choice_to_gemini(tool_choice)
+    if tool_cfg is not None:
+        body["toolConfig"] = tool_cfg
+
+    generation_config: Dict[str, Any] = {}
+    if isinstance(temperature, (int, float)):
+        generation_config["temperature"] = float(temperature)
+    if isinstance(max_tokens, int) and max_tokens > 0:
+        generation_config["maxOutputTokens"] = max_tokens
+    if isinstance(top_p, (int, float)):
+        generation_config["topP"] = float(top_p)
+    if isinstance(stop, str) and stop:
+        generation_config["stopSequences"] = [stop]
+    elif isinstance(stop, list) and stop:
+        generation_config["stopSequences"] = [str(s) for s in stop if s]
+    normalized_thinking = _normalize_thinking_config(thinking_config)
+    if normalized_thinking:
+        generation_config["thinkingConfig"] = normalized_thinking
+    if generation_config:
+        body["generationConfig"] = generation_config
+
+    return body
+
+
+def wrap_code_assist_request(
+    *,
+    project_id: str,
+    model: str,
+    inner_request: Dict[str, Any],
+    user_prompt_id: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Wrap the inner Gemini request in the Code Assist envelope."""
+    return {
+        "project": project_id,
+        "model": model,
+        "user_prompt_id": user_prompt_id or str(uuid.uuid4()),
+        "request": inner_request,
+    }
+
+
+# =============================================================================
+# Response translation: Gemini → OpenAI
+# =============================================================================
+
+def _translate_gemini_response(
+    resp: Dict[str, Any],
+    model: str,
+) -> SimpleNamespace:
+    """Non-streaming Gemini response -> OpenAI-shaped SimpleNamespace.
+
+    Code Assist wraps the actual Gemini response inside ``response``, so we
+    unwrap it first if present.
+    """
+    inner = resp.get("response") if isinstance(resp.get("response"), dict) else resp
+
+    candidates = inner.get("candidates") or []
+    if not isinstance(candidates, list) or not candidates:
+        return _empty_response(model)
+
+    cand = candidates[0]
+    content_obj = cand.get("content") if isinstance(cand, dict) else {}
+    parts = content_obj.get("parts") if isinstance(content_obj, dict) else []
+
+    text_pieces: List[str] = []
+    reasoning_pieces: List[str] = []
+    tool_calls: List[SimpleNamespace] = []
+
+    for i, part in enumerate(parts or []):
+        if not isinstance(part, dict):
+            continue
+        # Thought parts are model's internal reasoning — surface as reasoning,
+        # don't mix into content.
+        if part.get("thought") is True:
+            if isinstance(part.get("text"), str):
+                reasoning_pieces.append(part["text"])
+            continue
+        if isinstance(part.get("text"), str):
+            text_pieces.append(part["text"])
+            continue
+        fc = part.get("functionCall")
+        if isinstance(fc, dict) and fc.get("name"):
+            try:
+                args_str = json.dumps(fc.get("args") or {}, ensure_ascii=False)
+            except (TypeError, ValueError):
+                args_str = "{}"
+            tool_calls.append(SimpleNamespace(
+                id=f"call_{uuid.uuid4().hex[:12]}",
+                type="function",
+                index=i,
+                function=SimpleNamespace(name=str(fc["name"]), arguments=args_str),
+            ))
+
+    finish_reason = "tool_calls" if tool_calls else _map_gemini_finish_reason(
+        str(cand.get("finishReason") or "")
+    )
+
+    usage_meta = inner.get("usageMetadata") or {}
+    usage = SimpleNamespace(
+        prompt_tokens=int(usage_meta.get("promptTokenCount") or 0),
+        completion_tokens=int(usage_meta.get("candidatesTokenCount") or 0),
+        total_tokens=int(usage_meta.get("totalTokenCount") or 0),
+        prompt_tokens_details=SimpleNamespace(
+            cached_tokens=int(usage_meta.get("cachedContentTokenCount") or 0),
+        ),
+    )
+
+    message = SimpleNamespace(
+        role="assistant",
+        content="".join(text_pieces) if text_pieces else None,
+        tool_calls=tool_calls or None,
+        reasoning="".join(reasoning_pieces) or None,
+        reasoning_content="".join(reasoning_pieces) or None,
+        reasoning_details=None,
+    )
+    choice = SimpleNamespace(
+        index=0,
+        message=message,
+        finish_reason=finish_reason,
+    )
+    return SimpleNamespace(
+        id=f"chatcmpl-{uuid.uuid4().hex[:12]}",
+        object="chat.completion",
+        created=int(time.time()),
+        model=model,
+        choices=[choice],
+        usage=usage,
+    )
+
+
+def _empty_response(model: str) -> SimpleNamespace:
+    message = SimpleNamespace(
+        role="assistant", content="", tool_calls=None,
+        reasoning=None, reasoning_content=None, reasoning_details=None,
+    )
+    choice = SimpleNamespace(index=0, message=message, finish_reason="stop")
+    usage = SimpleNamespace(
+        prompt_tokens=0, completion_tokens=0, total_tokens=0,
+        prompt_tokens_details=SimpleNamespace(cached_tokens=0),
+    )
+    return SimpleNamespace(
+        id=f"chatcmpl-{uuid.uuid4().hex[:12]}",
+        object="chat.completion",
+        created=int(time.time()),
+        model=model,
+        choices=[choice],
+        usage=usage,
+    )
+
+
+def _map_gemini_finish_reason(reason: str) -> str:
+    mapping = {
+        "STOP": "stop",
+        "MAX_TOKENS": "length",
+        "SAFETY": "content_filter",
+        "RECITATION": "content_filter",
+        "OTHER": "stop",
+    }
+    return mapping.get(reason.upper(), "stop")
+
+
+# =============================================================================
+# Streaming SSE iterator
+# =============================================================================
+
+class _GeminiStreamChunk(SimpleNamespace):
+    """Mimics an OpenAI ChatCompletionChunk with .choices[0].delta."""
+    pass
+
+
+def _make_stream_chunk(
+    *,
+    model: str,
+    content: str = "",
+    tool_call_delta: Optional[Dict[str, Any]] = None,
+    finish_reason: Optional[str] = None,
+    reasoning: str = "",
+) -> _GeminiStreamChunk:
+    delta_kwargs: Dict[str, Any] = {"role": "assistant"}
+    if content:
+        delta_kwargs["content"] = content
+    if tool_call_delta is not None:
+        delta_kwargs["tool_calls"] = [SimpleNamespace(
+            index=tool_call_delta.get("index", 0),
+            id=tool_call_delta.get("id") or f"call_{uuid.uuid4().hex[:12]}",
+            type="function",
+            function=SimpleNamespace(
+                name=tool_call_delta.get("name") or "",
+                arguments=tool_call_delta.get("arguments") or "",
+            ),
+        )]
+    if reasoning:
+        delta_kwargs["reasoning"] = reasoning
+        delta_kwargs["reasoning_content"] = reasoning
+    delta = SimpleNamespace(**delta_kwargs)
+    choice = SimpleNamespace(index=0, delta=delta, finish_reason=finish_reason)
+    return _GeminiStreamChunk(
+        id=f"chatcmpl-{uuid.uuid4().hex[:12]}",
+        object="chat.completion.chunk",
+        created=int(time.time()),
+        model=model,
+        choices=[choice],
+        usage=None,
+    )
+
+
+def _iter_sse_events(response: httpx.Response) -> Iterator[Dict[str, Any]]:
+    """Parse Server-Sent Events from an httpx streaming response."""
+    buffer = ""
+    for chunk in response.iter_text():
+        if not chunk:
+            continue
+        buffer += chunk
+        while "\n" in buffer:
+            line, buffer = buffer.split("\n", 1)
+            line = line.rstrip("\r")
+            if not line:
+                continue
+            if line.startswith("data: "):
+                data = line[6:]
+                if data == "[DONE]":
+                    return
+                try:
+                    yield json.loads(data)
+                except json.JSONDecodeError:
+                    logger.debug("Non-JSON SSE line: %s", data[:200])
+
+
+def _translate_stream_event(
+    event: Dict[str, Any],
+    model: str,
+    tool_call_counter: List[int],
+) -> List[_GeminiStreamChunk]:
+    """Unwrap Code Assist envelope and emit OpenAI-shaped chunk(s).
+
+    ``tool_call_counter`` is a single-element list used as a mutable counter
+    across events in the same stream. Each ``functionCall`` part gets a
+    fresh, unique OpenAI ``index`` — keying by function name would collide
+    whenever the model issues parallel calls to the same tool (e.g. reading
+    three files in one turn).
+    """
+    inner = event.get("response") if isinstance(event.get("response"), dict) else event
+    candidates = inner.get("candidates") or []
+    if not candidates:
+        return []
+    cand = candidates[0]
+    if not isinstance(cand, dict):
+        return []
+
+    chunks: List[_GeminiStreamChunk] = []
+
+    content = cand.get("content") or {}
+    parts = content.get("parts") if isinstance(content, dict) else []
+    for part in parts or []:
+        if not isinstance(part, dict):
+            continue
+        if part.get("thought") is True and isinstance(part.get("text"), str):
+            chunks.append(_make_stream_chunk(
+                model=model, reasoning=part["text"],
+            ))
+            continue
+        if isinstance(part.get("text"), str) and part["text"]:
+            chunks.append(_make_stream_chunk(model=model, content=part["text"]))
+        fc = part.get("functionCall")
+        if isinstance(fc, dict) and fc.get("name"):
+            name = str(fc["name"])
+            idx = tool_call_counter[0]
+            tool_call_counter[0] += 1
+            try:
+                args_str = json.dumps(fc.get("args") or {}, ensure_ascii=False)
+            except (TypeError, ValueError):
+                args_str = "{}"
+            chunks.append(_make_stream_chunk(
+                model=model,
+                tool_call_delta={
+                    "index": idx,
+                    "name": name,
+                    "arguments": args_str,
+                },
+            ))
+
+    finish_reason_raw = str(cand.get("finishReason") or "")
+    if finish_reason_raw:
+        mapped = _map_gemini_finish_reason(finish_reason_raw)
+        if tool_call_counter[0] > 0:
+            mapped = "tool_calls"
+        chunks.append(_make_stream_chunk(model=model, finish_reason=mapped))
+    return chunks
+
+
+# =============================================================================
+# GeminiCloudCodeClient — OpenAI-compatible facade
+# =============================================================================
+
+MARKER_BASE_URL = "cloudcode-pa://google"
+
+
+class _GeminiChatCompletions:
+    def __init__(self, client: "GeminiCloudCodeClient"):
+        self._client = client
+
+    def create(self, **kwargs: Any) -> Any:
+        return self._client._create_chat_completion(**kwargs)
+
+
+class _GeminiChatNamespace:
+    def __init__(self, client: "GeminiCloudCodeClient"):
+        self.completions = _GeminiChatCompletions(client)
+
+
+class GeminiCloudCodeClient:
+    """Minimal OpenAI-SDK-compatible facade over Code Assist v1internal."""
+
+    def __init__(
+        self,
+        *,
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        default_headers: Optional[Dict[str, str]] = None,
+        project_id: str = "",
+        **_: Any,
+    ):
+        # `api_key` here is a dummy — real auth is the OAuth access token
+        # fetched on every call via agent.google_oauth.get_valid_access_token().
+        # We accept the kwarg for openai.OpenAI interface parity.
+        self.api_key = api_key or "google-oauth"
+        self.base_url = base_url or MARKER_BASE_URL
+        self._default_headers = dict(default_headers or {})
+        self._configured_project_id = project_id
+        self._project_context: Optional[ProjectContext] = None
+        self._project_context_lock = False  # simple single-thread guard
+        self.chat = _GeminiChatNamespace(self)
+        self.is_closed = False
+        self._http = httpx.Client(timeout=httpx.Timeout(connect=15.0, read=600.0, write=30.0, pool=30.0))
+
+    def close(self) -> None:
+        self.is_closed = True
+        try:
+            self._http.close()
+        except Exception:
+            pass
+
+    # Implement the OpenAI SDK's context-manager-ish closure check
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+    def _ensure_project_context(self, access_token: str, model: str) -> ProjectContext:
+        """Lazily resolve and cache the project context for this client."""
+        if self._project_context is not None:
+            return self._project_context
+
+        env_project = google_oauth.resolve_project_id_from_env()
+        creds = google_oauth.load_credentials()
+        stored_project = creds.project_id if creds else ""
+
+        # Prefer what's already baked into the creds
+        if stored_project:
+            self._project_context = ProjectContext(
+                project_id=stored_project,
+                managed_project_id=creds.managed_project_id if creds else "",
+                tier_id="",
+                source="stored",
+            )
+            return self._project_context
+
+        ctx = resolve_project_context(
+            access_token,
+            configured_project_id=self._configured_project_id,
+            env_project_id=env_project,
+            user_agent_model=model,
+        )
+        # Persist discovered project back to the creds file so the next
+        # session doesn't re-run the discovery.
+        if ctx.project_id or ctx.managed_project_id:
+            google_oauth.update_project_ids(
+                project_id=ctx.project_id,
+                managed_project_id=ctx.managed_project_id,
+            )
+        self._project_context = ctx
+        return ctx
+
+    def _create_chat_completion(
+        self,
+        *,
+        model: str = "gemini-2.5-flash",
+        messages: Optional[List[Dict[str, Any]]] = None,
+        stream: bool = False,
+        tools: Any = None,
+        tool_choice: Any = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        top_p: Optional[float] = None,
+        stop: Any = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Any = None,
+        **_: Any,
+    ) -> Any:
+        access_token = google_oauth.get_valid_access_token()
+        ctx = self._ensure_project_context(access_token, model)
+
+        thinking_config = None
+        if isinstance(extra_body, dict):
+            thinking_config = extra_body.get("thinking_config") or extra_body.get("thinkingConfig")
+
+        inner = build_gemini_request(
+            messages=messages or [],
+            tools=tools,
+            tool_choice=tool_choice,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            stop=stop,
+            thinking_config=thinking_config,
+        )
+        wrapped = wrap_code_assist_request(
+            project_id=ctx.project_id,
+            model=model,
+            inner_request=inner,
+        )
+
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "Authorization": f"Bearer {access_token}",
+            "User-Agent": "hermes-agent (gemini-cli-compat)",
+            "X-Goog-Api-Client": "gl-python/hermes",
+            "x-activity-request-id": str(uuid.uuid4()),
+        }
+        headers.update(self._default_headers)
+
+        if stream:
+            return self._stream_completion(model=model, wrapped=wrapped, headers=headers)
+
+        url = f"{CODE_ASSIST_ENDPOINT}/v1internal:generateContent"
+        response = self._http.post(url, json=wrapped, headers=headers)
+        if response.status_code != 200:
+            raise _gemini_http_error(response)
+        try:
+            payload = response.json()
+        except ValueError as exc:
+            raise CodeAssistError(
+                f"Invalid JSON from Code Assist: {exc}",
+                code="code_assist_invalid_json",
+            ) from exc
+        return _translate_gemini_response(payload, model=model)
+
+    def _stream_completion(
+        self,
+        *,
+        model: str,
+        wrapped: Dict[str, Any],
+        headers: Dict[str, str],
+    ) -> Iterator[_GeminiStreamChunk]:
+        """Generator that yields OpenAI-shaped streaming chunks."""
+        url = f"{CODE_ASSIST_ENDPOINT}/v1internal:streamGenerateContent?alt=sse"
+        stream_headers = dict(headers)
+        stream_headers["Accept"] = "text/event-stream"
+
+        def _generator() -> Iterator[_GeminiStreamChunk]:
+            try:
+                with self._http.stream("POST", url, json=wrapped, headers=stream_headers) as response:
+                    if response.status_code != 200:
+                        # Materialize error body for better diagnostics
+                        response.read()
+                        raise _gemini_http_error(response)
+                    tool_call_counter: List[int] = [0]
+                    for event in _iter_sse_events(response):
+                        for chunk in _translate_stream_event(event, model, tool_call_counter):
+                            yield chunk
+            except httpx.HTTPError as exc:
+                raise CodeAssistError(
+                    f"Streaming request failed: {exc}",
+                    code="code_assist_stream_error",
+                ) from exc
+
+        return _generator()
+
+
+def _gemini_http_error(response: httpx.Response) -> CodeAssistError:
+    """Translate an httpx response into a CodeAssistError with rich metadata.
+
+    Parses Google's error envelope (``{"error": {"code", "message", "status",
+    "details": [...]}}``) so the agent's error classifier can reason about
+    the failure — ``status_code`` enables the rate_limit / auth classification
+    paths, and ``response`` lets the main loop honor ``Retry-After`` just
+    like it does for OpenAI SDK exceptions.
+
+    Also lifts a few recognizable Google conditions into human-readable
+    messages so the user sees something better than a 500-char JSON dump:
+
+        MODEL_CAPACITY_EXHAUSTED → "Gemini model capacity exhausted for
+            <model>. This is a Google-side throttle..."
+        RESOURCE_EXHAUSTED w/o reason → quota-style message
+        404 → "Model <name> not found at cloudcode-pa..."
+    """
+    status = response.status_code
+
+    # Parse the body once, surviving any weird encodings.
+    body_text = ""
+    body_json: Dict[str, Any] = {}
+    try:
+        body_text = response.text
+    except Exception:
+        body_text = ""
+    if body_text:
+        try:
+            parsed = json.loads(body_text)
+            if isinstance(parsed, dict):
+                body_json = parsed
+        except (ValueError, TypeError):
+            body_json = {}
+
+    # Dig into Google's error envelope.  Shape is:
+    #   {"error": {"code": 429, "message": "...", "status": "RESOURCE_EXHAUSTED",
+    #              "details": [{"@type": ".../ErrorInfo", "reason": "MODEL_CAPACITY_EXHAUSTED",
+    #                           "metadata": {...}},
+    #                          {"@type": ".../RetryInfo", "retryDelay": "30s"}]}}
+    err_obj = body_json.get("error") if isinstance(body_json, dict) else None
+    if not isinstance(err_obj, dict):
+        err_obj = {}
+    err_status = str(err_obj.get("status") or "").strip()
+    err_message = str(err_obj.get("message") or "").strip()
+    _raw_details = err_obj.get("details")
+    err_details_list = _raw_details if isinstance(_raw_details, list) else []
+
+    # Extract google.rpc.ErrorInfo reason + metadata.  There may be more
+    # than one ErrorInfo (rare), so we pick the first one with a reason.
+    error_reason = ""
+    error_metadata: Dict[str, Any] = {}
+    retry_delay_seconds: Optional[float] = None
+    for detail in err_details_list:
+        if not isinstance(detail, dict):
+            continue
+        type_url = str(detail.get("@type") or "")
+        if not error_reason and type_url.endswith("/google.rpc.ErrorInfo"):
+            reason = detail.get("reason")
+            if isinstance(reason, str) and reason:
+                error_reason = reason
+            md = detail.get("metadata")
+            if isinstance(md, dict):
+                error_metadata = md
+        elif retry_delay_seconds is None and type_url.endswith("/google.rpc.RetryInfo"):
+            # retryDelay is a google.protobuf.Duration string like "30s" or "1.5s".
+            delay_raw = detail.get("retryDelay")
+            if isinstance(delay_raw, str) and delay_raw.endswith("s"):
+                try:
+                    retry_delay_seconds = float(delay_raw[:-1])
+                except ValueError:
+                    pass
+            elif isinstance(delay_raw, (int, float)):
+                retry_delay_seconds = float(delay_raw)
+
+    # Fall back to the Retry-After header if the body didn't include RetryInfo.
+    if retry_delay_seconds is None:
+        try:
+            header_val = response.headers.get("Retry-After") or response.headers.get("retry-after")
+        except Exception:
+            header_val = None
+        if header_val:
+            try:
+                retry_delay_seconds = float(header_val)
+            except (TypeError, ValueError):
+                retry_delay_seconds = None
+
+    # Classify the error code.  ``code_assist_rate_limited`` stays the default
+    # for 429s; a more specific reason tag helps downstream callers (e.g. tests,
+    # logs) without changing the rate_limit classification path.
+    code = f"code_assist_http_{status}"
+    if status == 401:
+        code = "code_assist_unauthorized"
+    elif status == 429:
+        code = "code_assist_rate_limited"
+        if error_reason == "MODEL_CAPACITY_EXHAUSTED":
+            code = "code_assist_capacity_exhausted"
+
+    # Build a human-readable message.  Keep the status + a raw-body tail for
+    # debugging, but lead with a friendlier summary when we recognize the
+    # Google signal.
+    model_hint = ""
+    if isinstance(error_metadata, dict):
+        model_hint = str(error_metadata.get("model") or error_metadata.get("modelId") or "").strip()
+
+    if status == 429 and error_reason == "MODEL_CAPACITY_EXHAUSTED":
+        target = model_hint or "this Gemini model"
+        message = (
+            f"Gemini capacity exhausted for {target} (Google-side throttle, "
+            f"not a Hermes issue). Try a different Gemini model or set a "
+            f"fallback_providers entry to a non-Gemini provider."
+        )
+        if retry_delay_seconds is not None:
+            message += f" Google suggests retrying in {retry_delay_seconds:g}s."
+    elif status == 429 and err_status == "RESOURCE_EXHAUSTED":
+        message = (
+            f"Gemini quota exhausted ({err_message or 'RESOURCE_EXHAUSTED'}). "
+            f"Check /gquota for remaining daily requests."
+        )
+        if retry_delay_seconds is not None:
+            message += f" Retry suggested in {retry_delay_seconds:g}s."
+    elif status == 404:
+        # Google returns 404 when a model has been retired or renamed.
+        target = model_hint or (err_message or "model")
+        message = (
+            f"Code Assist 404: {target} is not available at "
+            f"cloudcode-pa.googleapis.com. It may have been renamed or "
+            f"retired. Check hermes_cli/models.py for the current list."
+        )
+    elif err_message:
+        # Generic fallback with the parsed message.
+        message = f"Code Assist HTTP {status} ({err_status or 'error'}): {err_message}"
+    else:
+        # Last-ditch fallback — raw body snippet.
+        message = f"Code Assist returned HTTP {status}: {body_text[:500]}"
+
+    return CodeAssistError(
+        message,
+        code=code,
+        status_code=status,
+        response=response,
+        retry_after=retry_delay_seconds,
+        details={
+            "status": err_status,
+            "reason": error_reason,
+            "metadata": error_metadata,
+            "message": err_message,
+        },
+    )