@@ -0,0 +1,25 @@
+// Copyright (c) 2025-2026 Huawei Technologies Co., Ltd. All rights reserved.
+
+/** Read a declared output capability independently of request parameter defaults. */
+export function profileOutputLimit(
+  context: unknown,
+  windowOverride?: number | null
+): number | undefined {
+  if (!context || typeof context !== "object" || !("output_limit" in context))
+    return undefined;
+  const output = context.output_limit;
+  if (
+    typeof output !== "number" ||
+    !Number.isSafeInteger(output) ||
+    output <= 0
+  )
+    return undefined;
+  const window =
+    windowOverride ??
+    ("combined_window" in context ? context.combined_window : undefined);
+  return typeof window === "number" &&
+    Number.isSafeInteger(window) &&
+    window > 0
+    ? Math.min(output, window)
+    : output;
+}
@@ -0,0 +1,31 @@
+// Copyright (c) 2025-2026 Huawei Technologies Co., Ltd. All rights reserved.
+
+import { describe, expect, it } from "vitest";
+import { profileOutputLimit } from "../../lib/modelProfileLimits";
+
+describe("profile output cap display", () => {
+  it("uses a known cap within the effective context window", () => {
+    expect(
+      profileOutputLimit({ output_limit: 384000, combined_window: 1000000 })
+    ).toBe(384000);
+    expect(
+      profileOutputLimit(
+        { output_limit: 384000, combined_window: 1000000 },
+        200000
+      )
+    ).toBe(200000);
+  });
+  it("leaves unknown or invalid caps empty", () => {
+    for (const context of [
+      undefined,
+      null,
+      {},
+      { output_limit: 0 },
+      { output_limit: -1 },
+      { output_limit: "8192" },
+      { output_limit: NaN },
+    ]) {
+      expect(profileOutputLimit(context)).toBeUndefined();
+    }
+  });
+});
@@ -0,0 +1,307 @@
+# Copyright (c) 2025-2026 Huawei Technologies Co., Ltd. All rights reserved.
+
+"""OpenAI-compatible chat framing; runtime execution and retries stay outside."""
+
+import json
+import math
+
+from pydantic import ValidationError
+
+from nexent.core.model_service.contracts import AssistantMessage, ModelRequest, TokenUsage, ToolCall
+from nexent.core.model_service.http_contracts import PreparedHttpRequest, RawHttpResponse
+from nexent.core.model_service.parameter_transforms import parameter_transform_references, transform_parameter
+from nexent.core.model_service.profiles.contracts import VersionedReference
+from nexent.core.model_service.profiles.instruction_mapping import validate_instruction_mapping
+from nexent.core.model_service.profiles.parameter_bindings import validate_parameter_bindings
+from nexent.core.model_service.profiles.parameter_rules import compile_parameter_rules, validate_saved_parameter_values
+from nexent.core.model_service.profiles.parameter_schema import ParameterValidationError
+from nexent.core.model_service.profiles.request_parameters import _set, _tokens, apply_extra_api_parameters
+from nexent.core.model_service.profiles.resolved_profile import ResolvedModelProfile
+
+
+class ModelAdapterError(ValueError):
+    """Fixed protocol category without model text, tool arguments or raw body."""
+
+    def __init__(self, category: str, *, response: RawHttpResponse | None = None):
+        self.category = category
+        self.response = response
+        super().__init__(category)
+
+
+def _unique_object(pairs):
+    result = {}
+    for key, value in pairs:
+        if key in result:
+            raise ValueError("Duplicate JSON member")
+        result[key] = value
+    return result
+
+
+def _invalid_constant(value):
+    raise ValueError("Invalid JSON constant")
+
+
+def _finite_float(value):
+    number = float(value)
+    if not math.isfinite(number):
+        raise ValueError("Invalid JSON number")
+    return number
+
+
+def parse_json(text: str):
+    return json.loads(
+        text, object_pairs_hook=_unique_object, parse_constant=_invalid_constant, parse_float=_finite_float
+    )
+
+
+def _content_parts(value):
+    if value is None:
+        return []
+    if isinstance(value, str):
+        return [{"type": "text", "text": value}]
+    if isinstance(value, list) and all(isinstance(part, dict) and isinstance(part.get("type"), str) for part in value):
+        return value
+    raise ModelAdapterError("MODEL_RESPONSE_INVALID")
+
+
+def _usage(raw, semantics_id):
+    if raw is None:
+        return TokenUsage(semantics_id=semantics_id)
+    if not isinstance(raw, dict):
+        raise ModelAdapterError("MODEL_USAGE_INVALID")
+    details = []
+    for key in ("prompt_tokens_details", "completion_tokens_details"):
+        value = raw.get(key)
+        if value is None:
+            value = {}
+        if not isinstance(value, dict):
+            raise ModelAdapterError("MODEL_USAGE_INVALID")
+        details.append(value)
+    try:
+        return TokenUsage(
+            input_tokens=raw.get("prompt_tokens"),
+            output_tokens=raw.get("completion_tokens"),
+            total_tokens=raw.get("total_tokens"),
+            cached_input_tokens=details[0].get("cached_tokens"),
+            reasoning_tokens=details[1].get("reasoning_tokens"),
+            origin="provider",
+            semantics_id=semantics_id,
+        )
+    except ValidationError:
+        raise ModelAdapterError("MODEL_USAGE_INVALID") from None
+
+
+def _tool_call(raw, index):
+    if not isinstance(raw, dict) or raw.get("type") != "function" or not isinstance(raw.get("function"), dict):
+        raise ModelAdapterError("MODEL_TOOL_CALL_INVALID")
+    function = raw["function"]
+    arguments = function.get("arguments")
+    if not isinstance(arguments, str):
+        raise ModelAdapterError("MODEL_TOOL_CALL_INVALID")
+    parsed, error = None, None
+    try:
+        parsed = parse_json(arguments)
+        if not isinstance(parsed, dict):
+            raise ValueError("Tool arguments must be an object")
+    except (ValueError, RecursionError):
+        parsed, error = None, "INVALID_TOOL_ARGUMENTS"
+    try:
+        return ToolCall(
+            provider_call_id=raw.get("id"),
+            index=index,
+            name=function.get("name"),
+            raw_arguments=arguments,
+            parsed_arguments=parsed,
+            argument_error=error,
+        )
+    except ValidationError:
+        raise ModelAdapterError("MODEL_TOOL_CALL_INVALID") from None
+
+
+class OpenAIChatAdapter:
+    """Single-choice messages with declared parameter bindings and wire metadata."""
+
+    reference = VersionedReference(ref="openai_chat_v1", version=1)
+    protocol_reference = VersionedReference(ref="openai_chat_completions", version=1)
+
+    def __init__(self, *, reasoning_replay: bool = False):
+        if type(reasoning_replay) is not bool:
+            raise ValueError("Reasoning replay declaration must be boolean")
+        self._reasoning_replay = reasoning_replay
+
+    def prepare_http_request(self, request: ModelRequest, profile: ResolvedModelProfile) -> PreparedHttpRequest:
+        if request.outlet_id != profile.outlet_id or request.config_revision != profile.config_revision:
+            raise ModelAdapterError("MODEL_SNAPSHOT_MISMATCH")
+        if profile.bindings.protocol != self.protocol_reference:
+            raise ModelAdapterError("MODEL_PROTOCOL_UNSUPPORTED")
+        validate_instruction_mapping(profile.instruction_mapping)
+        if request.tools and profile.capabilities.tools.status != "supported":
+            raise ModelAdapterError("NATIVE_TOOL_PROTOCOL_UNAVAILABLE")
+        mappings = profile.instruction_mapping.mappings.model_dump()
+        messages = []
+        for message in request.messages:
+            role = mappings.get(message.semantic_role, message.semantic_role)
+            if role not in profile.instruction_mapping.supported_roles:
+                raise ModelAdapterError("MODEL_ROLE_UNSUPPORTED")
+            if message.tool_calls and role != "assistant" or message.tool_call_id is not None and role != "tool":
+                raise ModelAdapterError("MODEL_MESSAGE_INVALID")
+            if role == "tool" and message.tool_call_id is None:
+                raise ModelAdapterError("MODEL_MESSAGE_INVALID")
+            if (
+                message.tool_calls or message.tool_call_id is not None or role == "tool"
+            ) and profile.capabilities.tools.status != "supported":
+                raise ModelAdapterError("NATIVE_TOOL_PROTOCOL_UNAVAILABLE")
+            parts = message.content_parts
+            if any(
+                part.get("type")
+                not in {"text", "image_url", "input_audio", "audio_url", "video_url", "input_image", "input_video"}
+                for part in parts
+            ):
+                raise ModelAdapterError("MODEL_CONTENT_UNSUPPORTED")
+            if any(part.get("type") == "text" and not isinstance(part.get("text"), str) for part in parts):
+                raise ModelAdapterError("MODEL_CONTENT_UNSUPPORTED")
+            simple_text = len(parts) == 1 and set(parts[0]) == {"type", "text"}
+            wire = {"role": role, "content": parts[0]["text"] if simple_text else parts}
+            if not parts:
+                wire["content"] = None if role == "assistant" and message.tool_calls else ""
+            if message.tool_call_id is not None:
+                wire["tool_call_id"] = message.tool_call_id
+            if message.tool_calls:
+                wire["tool_calls"] = [
+                    {
+                        "id": call.provider_call_id,
+                        "type": "function",
+                        "function": {"name": call.name, "arguments": call.raw_arguments},
+                    }
+                    for call in message.tool_calls
+                ]
+            if message.reasoning_parts:
+                if (
+                    not self._reasoning_replay
+                    or role != "assistant"
+                    or any(
+                        part.get("type") != "text" or not isinstance(part.get("text"), str)
+                        for part in message.reasoning_parts
+                    )
+                ):
+                    raise ModelAdapterError("MODEL_REASONING_FORMAT_UNSUPPORTED")
+                wire["reasoning_content"] = "".join(part["text"] for part in message.reasoning_parts)
+            messages.append(wire)
+        body = {"model": profile.identity.request_model_id, "messages": messages, "stream": request.stream}
+        if request.tools:
+            body["tools"] = [
+                {
+                    "type": "function",
+                    "function": {"name": tool.name, "description": tool.description, "parameters": tool.input_schema},
+                }
+                for tool in request.tools
+            ]
+        if request.stream:
+            body["stream_options"] = {"include_usage": True}
+        try:
+            rules = compile_parameter_rules(profile.parameter_rules)
+            validate_saved_parameter_values(rules, profile.parameter_values)
+            bindings = validate_parameter_bindings(rules, parameter_transform_references())
+            for key, binding in bindings.items():
+                if key in profile.parameter_values:
+                    _set(body, _tokens(binding["path"]), transform_parameter(binding, profile.parameter_values[key]),
+                         binding["path"])
+            body = apply_extra_api_parameters(
+                body, profile.extra_api_parameters, parameter_paths=[binding["path"] for binding in bindings.values()]
+            )
+            return PreparedHttpRequest(
+                request_id=request.request_id,
+                method=profile.connection_rules.method,
+                base_url=profile.connection.base_url,
+                relative_path=profile.connection_rules.relative_path,
+                headers=profile.connection_rules.headers,
+                credential_ref=profile.connection.credential_ref,
+                json_body=body,
+                timeout_seconds=profile.connection.timeout_seconds,
+                ssl_verify=profile.connection.ssl_verify,
+                stream_protocol="sse" if request.stream else None,
+            )
+        except (ParameterValidationError, ValueError, TypeError):
+            raise ModelAdapterError("MODEL_REQUEST_CONFIGURATION_INVALID") from None
+
+    def decode_response(
+        self, raw: RawHttpResponse, request: ModelRequest, profile: ResolvedModelProfile
+    ) -> AssistantMessage:
+        try:
+            body = parse_json(raw.body.decode("utf-8"))
+            if isinstance(body, dict) and body.get("error") is not None:
+                raise ModelAdapterError("MODEL_PROVIDER_ERROR", response=raw)
+            if not isinstance(body, dict) or not isinstance(body.get("choices"), list) or len(body["choices"]) != 1:
+                raise ModelAdapterError("MODEL_RESPONSE_INVALID")
+            choice = body["choices"][0]
+            if not isinstance(choice, dict) or type(choice.get("index")) is not int or choice["index"] != 0:
+                raise ModelAdapterError("MODEL_RESPONSE_INVALID")
+            message = choice.get("message")
+            if not isinstance(message, dict) or message.get("role") != "assistant":
+                raise ModelAdapterError("MODEL_RESPONSE_INVALID")
+            finish = choice.get("finish_reason")
+            if not isinstance(finish, str) or not finish.strip():
+                raise ModelAdapterError("MODEL_RESPONSE_INCOMPLETE")
+            calls = message.get("tool_calls")
+            if calls is None:
+                calls = []
+            if not isinstance(calls, list):
+                raise ModelAdapterError("MODEL_TOOL_CALL_INVALID")
+            parts = _content_parts(message.get("content"))
+            if message.get("audio") is not None:
+                parts.append({"type": "audio", "audio": message["audio"]})
+            return AssistantMessage(
+                content_parts=parts,
+                reasoning_parts=_content_parts(message.get("reasoning_content")),
+                refusal=message.get("refusal"),
+                tool_calls=[_tool_call(call, index) for index, call in enumerate(calls)],
+                finish_reason=finish,
+                usage=_usage(body.get("usage"), profile.capabilities.usage.semantics_id),
+                provider_metadata={
+                    "response_id": body.get("id"),
+                    "model": body.get("model"),
+                    "request_id": request.request_id,
+                    "unmapped_message_fields": {
+                        key: value
+                        for key, value in message.items()
+                        if key not in {"role", "content", "reasoning_content", "refusal", "tool_calls", "audio"}
+                    },
+                },
+            )
+        except ModelAdapterError:
+            raise
+        except (ValueError, TypeError, UnicodeError, RecursionError):
+            raise ModelAdapterError("MODEL_RESPONSE_INVALID") from None
+
+    def new_stream_decoder(self, request, profile):
+        """Import the stream state lazily to keep complete-message helpers reusable."""
+        from nexent.core.model_service.adapters.openai_stream import OpenAIStreamDecoder
+
+        return OpenAIStreamDecoder(request, profile)
+
+    def normalize_error(self, error, *, request_id, attempt=1, max_attempts=1, will_retry=False):
+        from nexent.core.model_service.errors import normalize_model_error
+
+        return normalize_model_error(
+            error, request_id=request_id, attempt=attempt, max_attempts=max_attempts, will_retry=will_retry
+        )
+
+    def decode_stream(self, raw_events, request, profile):
+        """Stop at the verified completion marker; HTTP ownership is external."""
+        decoder = self.new_stream_decoder(request, profile)
+        for frame in raw_events:
+            for event in decoder.push(frame):
+                yield event
+                if event.type == "message_completed":
+                    return
+        yield from decoder.finish()
+
+    async def adecode_stream(self, raw_events, request, profile):
+        decoder = self.new_stream_decoder(request, profile)
+        async for frame in raw_events:
+            for event in decoder.push(frame):
+                yield event
+                if event.type == "message_completed":
+                    return
+        for event in decoder.finish():
+            yield event