diff --git a/docs/supported-integrations/oci-generative-ai.mdx b/docs/supported-integrations/oci-generative-ai.mdx
new file mode 100644
index 000000000..15dfd3f7f
--- /dev/null
+++ b/docs/supported-integrations/oci-generative-ai.mdx
@@ -0,0 +1,124 @@
+---
+title: "NeMo Relay OCI Generative AI Integration"
+sidebar-title: "OCI Generative AI Integration Guide"
+description: "Add NeMo Relay observability to LangChain agents backed by Oracle Cloud Infrastructure (OCI) Generative AI."
+position: 6
+---
+{/* SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: Apache-2.0 */}
+
+
+Use the `nemo_relay.integrations.langchain` package to add NeMo Relay
+observability to LangChain agents that call
+[Oracle Cloud Infrastructure (OCI) Generative AI](https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/)
+through the [`langchain-oci`](https://pypi.org/project/langchain-oci/) provider.
+
+OCI Generative AI serves managed on-demand models (Meta Llama, Cohere Command,
+xAI Grok, Google Gemini, and OpenAI models) and dedicated AI cluster endpoints,
+including imported open-weights models such as NVIDIA Nemotron. The same
+integration path covers both.
+
+## Setup
+
+Install the LangChain integration extra together with the OCI provider extra in
+your application environment.
+
+
+
+```bash
+uv add "nemo-relay[langchain,langchain-oci]"
+```
+
+
+
+```bash
+pip install "nemo-relay[langchain,langchain-oci]"
+```
+
+
+
+
+The `langchain-oci` provider authenticates with the OCI SDK. All standard OCI
+authentication types work: API key, session token, instance principal, and
+resource principal. Configure a profile in `~/.oci/config` or run on an OCI
+compute instance with an instance principal.
+
+## Usage Example
+
+```python
+import asyncio
+
+import nemo_relay
+from langchain.agents import create_agent
+from langchain_core.tools import tool
+from langchain_oci import ChatOCIGenAI
+from nemo_relay.integrations.langchain import NemoRelayCallbackHandler, NemoRelayMiddleware
+
+
+@tool
+def get_weather(location: str) -> str:
+ """Get the current weather for a location."""
+ return f"The weather in {location} is sunny and 72 degrees."
+
+
+model = ChatOCIGenAI(
+ model_id="meta.llama-3.3-70b-instruct",
+ service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+ compartment_id="",
+ auth_type="API_KEY",
+ model_kwargs={"temperature": 0.0, "max_tokens": 512},
+)
+
+agent = create_agent(
+ model=model,
+ tools=[get_weather],
+ middleware=[NemoRelayMiddleware()],
+ system_prompt="Use tools when they are relevant. Keep the final answer brief.",
+)
+
+input_payload = {
+ "messages": [
+ {
+ "role": "user",
+ "content": "What is the weather in San Francisco?",
+ }
+ ]
+}
+
+with nemo_relay.scope.scope("langchain-request", nemo_relay.ScopeType.Agent):
+ result = asyncio.run(
+ agent.ainvoke(input_payload, config={"callbacks": [NemoRelayCallbackHandler()]})
+ )
+
+final_message = result["messages"][-1]
+print(f"Final response: {final_message.content}")
+```
+
+## Dedicated AI Cluster Endpoints
+
+To call a model hosted on an OCI dedicated AI cluster — including imported
+open-weights models such as NVIDIA Nemotron — pass the endpoint OCID as the
+model ID. Everything else, including the NeMo Relay integration, stays the
+same:
+
+```python
+model = ChatOCIGenAI(
+ model_id="ocid1.generativeaiendpoint.oc1..",
+ service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+ compartment_id="",
+ auth_type="API_KEY",
+ provider="meta",
+)
+```
+
+## Verify the Integration
+
+The integration works correctly when:
+
+- The agent run completes and prints a final response.
+- The `langchain-request` scope contains the managed model call.
+- Tool activity appears under the same request when the agent decides to use `get_weather`.
+
+## Observability
+
+Refer to [Observability](/configure-plugins/observability/about) for details on exporting NeMo Relay observability data to third-party systems.
diff --git a/pyproject.toml b/pyproject.toml
index cb833ab4e..dfd1bc98b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -91,6 +91,11 @@ langchain-nvidia = [
"aiohttp>=3.14.1",
]
+langchain-oci = [
+ "nemo-relay[langchain]",
+ "langchain-oci>=0.2.4,<1.0.0",
+]
+
[tool.maturin]
manifest-path = "crates/python/Cargo.toml"
python-source = "python"
diff --git a/python/nemo_relay/providers/__init__.py b/python/nemo_relay/providers/__init__.py
new file mode 100644
index 000000000..eeefd62f3
--- /dev/null
+++ b/python/nemo_relay/providers/__init__.py
@@ -0,0 +1,16 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Provider-specific request and response codecs for ``nemo_relay.llm``.
+
+Modules in this package implement the ``nemo_relay.codecs.LlmCodec`` and
+``nemo_relay.codecs.LlmResponseCodec`` protocols for LLM providers whose wire
+formats are not covered by the built-in OpenAI and Anthropic codecs.
+"""
+
+from nemo_relay.providers.oci_genai import OCIGenAIChatCodec, OCIGenAIResponseCodec
+
+__all__ = [
+ "OCIGenAIChatCodec",
+ "OCIGenAIResponseCodec",
+]
diff --git a/python/nemo_relay/providers/oci_genai.py b/python/nemo_relay/providers/oci_genai.py
new file mode 100644
index 000000000..39d11d78c
--- /dev/null
+++ b/python/nemo_relay/providers/oci_genai.py
@@ -0,0 +1,407 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Request and response codecs for Oracle Cloud Infrastructure (OCI) Generative AI.
+
+The OCI Generative AI chat API wraps requests in a ``ChatDetails`` envelope
+(``compartmentId``, ``servingMode``, ``chatRequest``) and supports two chat
+request formats selected by ``chatRequest.apiFormat``:
+
+- ``GENERIC``: OpenAI-style ``messages`` whose ``content`` is a list of typed
+ parts (``{"type": "TEXT", "text": ...}``). Used by Meta Llama, Google,
+ xAI, OpenAI, and imported open-weights models (for example NVIDIA Nemotron)
+ hosted on dedicated AI clusters.
+- ``COHERE``: a single ``message`` string plus ``chatHistory`` turns with
+ ``USER``/``CHATBOT``/``SYSTEM`` roles. Used by Cohere Command models.
+
+``OCIGenAIChatCodec`` normalizes both formats into ``AnnotatedLLMRequest`` so
+request intercepts (PII redaction, guardrails, policy) can operate on a single
+shape, and merges intercept edits back without dropping provider-specific
+fields. ``OCIGenAIResponseCodec`` normalizes ``ChatResult`` responses for
+``LLMEnd`` event annotation.
+
+Example::
+
+ import nemo_relay
+ from nemo_relay.providers.oci_genai import OCIGenAIChatCodec, OCIGenAIResponseCodec
+
+ result = await nemo_relay.llm.execute(
+ "oci-genai",
+ nemo_relay.LLMRequest({}, chat_details),
+ call_oci_chat,
+ codec=OCIGenAIChatCodec(),
+ response_codec=OCIGenAIResponseCodec(),
+ )
+"""
+
+from __future__ import annotations
+
+import typing
+
+from nemo_relay import Json
+from nemo_relay._native import AnnotatedLLMRequest, AnnotatedLLMResponse, LLMRequest
+
+# OCI parameter name -> normalized GenerationParams field. Parameters without
+# a normalized slot (topK, penalties, seed, ...) stay untouched in the raw
+# payload, which encode() preserves.
+_GENERIC_PARAM_MAP = {"maxTokens": "max_tokens", "temperature": "temperature", "topP": "top_p", "stop": "stop"}
+_COHERE_PARAM_MAP = {
+ "maxTokens": "max_tokens",
+ "temperature": "temperature",
+ "topP": "top_p",
+ "stopSequences": "stop",
+}
+
+_COHERE_ROLE_TO_NORMALIZED = {"USER": "user", "CHATBOT": "assistant", "SYSTEM": "system", "TOOL": "tool"}
+_NORMALIZED_ROLE_TO_COHERE = {v: k for k, v in _COHERE_ROLE_TO_NORMALIZED.items()}
+
+
+def _get_first(mapping: typing.Mapping[str, typing.Any], *keys: str) -> typing.Any:
+ """Return the first present key from ``mapping`` across naming conventions.
+
+ The OCI SDKs emit camelCase JSON while the OCI CLI emits kebab-case.
+ Callers pass camelCase keys; kebab-case and snake_case fallbacks are
+ derived automatically.
+ """
+ for key in keys:
+ for candidate in (key, _camel_to_kebab(key), _camel_to_snake(key)):
+ if candidate in mapping:
+ return mapping[candidate]
+ return None
+
+
+def _camel_to_kebab(key: str) -> str:
+ return "".join(f"-{c.lower()}" if c.isupper() else c for c in key)
+
+
+def _camel_to_snake(key: str) -> str:
+ return "".join(f"_{c.lower()}" if c.isupper() else c for c in key)
+
+
+def _generic_content_to_text(content: typing.Any) -> typing.Any:
+ """Flatten a GENERIC content-part list into plain text when possible."""
+ if isinstance(content, str) or content is None:
+ return content
+ if isinstance(content, list):
+ texts: list[str] = []
+ for part in content:
+ if isinstance(part, dict) and _get_first(part, "type") == "TEXT":
+ texts.append(str(_get_first(part, "text") or ""))
+ else:
+ # Non-text part (image, etc.): leave the list untouched.
+ return content
+ return "".join(texts)
+ return content
+
+
+def _text_to_generic_content(content: typing.Any) -> typing.Any:
+ """Wrap plain text back into the GENERIC typed content-part list."""
+ if isinstance(content, str):
+ return [{"type": "TEXT", "text": content}]
+ return content
+
+
+def _normalize_usage(usage: typing.Any) -> dict[str, typing.Any] | None:
+ """Map OCI usage counters onto the normalized ``Usage`` field names."""
+ if not isinstance(usage, dict):
+ return None
+ normalized: dict[str, typing.Any] = {}
+ prompt_tokens = _get_first(usage, "promptTokens")
+ completion_tokens = _get_first(usage, "completionTokens")
+ total_tokens = _get_first(usage, "totalTokens")
+ if prompt_tokens is not None:
+ normalized["prompt_tokens"] = prompt_tokens
+ if completion_tokens is not None:
+ normalized["completion_tokens"] = completion_tokens
+ if total_tokens is not None:
+ normalized["total_tokens"] = total_tokens
+ return normalized or None
+
+
+def _oci_tool_call_to_normalized(tool_call: typing.Any) -> typing.Any:
+ """Convert a flat OCI ``toolCalls`` entry into the normalized nested shape."""
+ if not isinstance(tool_call, dict) or "function" in tool_call:
+ return tool_call
+ return {
+ "id": _get_first(tool_call, "id"),
+ "type": "function",
+ "function": {
+ "name": _get_first(tool_call, "name"),
+ "arguments": _get_first(tool_call, "arguments"),
+ },
+ }
+
+
+def _normalized_tool_call_to_oci(tool_call: typing.Any) -> typing.Any:
+ """Convert a normalized nested tool call back into the flat OCI shape."""
+ if not isinstance(tool_call, dict) or "function" not in tool_call:
+ return tool_call
+ function = tool_call.get("function") or {}
+ return {
+ "id": tool_call.get("id"),
+ "type": "FUNCTION",
+ "name": function.get("name"),
+ "arguments": function.get("arguments"),
+ }
+
+
+class OCIGenAIChatCodec:
+ """Request codec for the OCI Generative AI chat API.
+
+ Accepts either a full ``ChatDetails`` envelope or a bare ``chatRequest``
+ payload, in the ``GENERIC`` or ``COHERE`` API format. Provider-specific
+ fields that have no normalized equivalent are carried in
+ ``AnnotatedLLMRequest.extra`` and restored by ``encode()``.
+ """
+
+ def decode(self, request: LLMRequest) -> AnnotatedLLMRequest:
+ """Decode an OCI chat payload into ``AnnotatedLLMRequest``."""
+ content = dict(request.content)
+ chat_request = _get_first(content, "chatRequest")
+ envelope = content if isinstance(chat_request, dict) else None
+ chat_request = dict(chat_request) if isinstance(chat_request, dict) else content
+
+ api_format = str(_get_first(chat_request, "apiFormat") or "GENERIC").upper()
+ model = self._model_from_envelope(envelope)
+
+ if api_format == "COHERE":
+ messages, params = self._decode_cohere(chat_request)
+ else:
+ messages, params = self._decode_generic(chat_request)
+
+ tools = _get_first(chat_request, "tools")
+ tool_choice = _get_first(chat_request, "toolChoice")
+
+ return AnnotatedLLMRequest(
+ messages,
+ model=model,
+ params=params or None,
+ tools=tools if isinstance(tools, list) else None,
+ tool_choice=tool_choice,
+ api_specific={"api": "custom", "api_name": "oci_genai", "data": {"apiFormat": api_format}},
+ extra={"envelope": envelope, "chatRequest": chat_request},
+ )
+
+ def encode(self, annotated: AnnotatedLLMRequest, original: LLMRequest) -> LLMRequest:
+ """Merge annotated edits back into the original OCI payload.
+
+ Edits are applied by comparing the annotation against a decoded
+ baseline of the original request and patching only changed fields, so
+ ``encode(decode(original), original)`` returns the original payload at
+ the JSON-value level and unmodeled provider fields survive edits.
+ """
+ content = dict(original.content)
+ chat_request_value = _get_first(content, "chatRequest")
+ has_envelope = isinstance(chat_request_value, dict)
+ chat_request = dict(chat_request_value) if has_envelope else dict(content)
+
+ api_specific = annotated.api_specific or {}
+ api_data_value = api_specific.get("data")
+ api_data: dict[str, typing.Any] = api_data_value if isinstance(api_data_value, dict) else {}
+ api_format = str(api_data.get("apiFormat") or _get_first(chat_request, "apiFormat") or "GENERIC").upper()
+
+ baseline = self.decode(original)
+ edited_messages = [dict(message) for message in annotated.messages]
+ baseline_messages = [dict(message) for message in baseline.messages]
+
+ if edited_messages != baseline_messages:
+ if api_format == "COHERE":
+ self._encode_cohere(chat_request, annotated)
+ else:
+ self._patch_generic_messages(chat_request, edited_messages, baseline_messages)
+
+ edited_params = dict(annotated.params or {})
+ baseline_params = dict(baseline.params or {})
+ if edited_params != baseline_params:
+ param_map = _COHERE_PARAM_MAP if api_format == "COHERE" else _GENERIC_PARAM_MAP
+ normalized_to_oci = {v: k for k, v in param_map.items()}
+ for key, value in edited_params.items():
+ if value is None or baseline_params.get(key) == value:
+ continue
+ chat_request[normalized_to_oci.get(key, key)] = value
+
+ if has_envelope:
+ content["chatRequest"] = chat_request
+ return LLMRequest(original.headers, content)
+ return LLMRequest(original.headers, chat_request)
+
+ @classmethod
+ def _patch_generic_messages(
+ cls,
+ chat_request: dict[str, typing.Any],
+ edited_messages: list[dict[str, typing.Any]],
+ baseline_messages: list[dict[str, typing.Any]],
+ ) -> None:
+ """Rewrite only the GENERIC messages that intercepts actually changed.
+
+ Unchanged messages are carried over from the raw payload verbatim so
+ per-message provider fields without a normalized equivalent survive.
+ """
+ raw_messages = _get_first(chat_request, "messages") or []
+ patched: list[typing.Any] = []
+ for index, edited in enumerate(edited_messages):
+ unchanged = index < len(baseline_messages) and edited == baseline_messages[index]
+ if unchanged and index < len(raw_messages):
+ patched.append(raw_messages[index])
+ else:
+ patched.append(cls._build_generic_message(edited))
+ chat_request["messages"] = patched
+
+ @staticmethod
+ def _build_generic_message(message: typing.Mapping[str, typing.Any]) -> dict[str, typing.Any]:
+ encoded: dict[str, typing.Any] = {
+ "role": str(message.get("role") or "user").upper(),
+ "content": _text_to_generic_content(message.get("content")),
+ }
+ if "tool_calls" in message:
+ encoded["toolCalls"] = [_normalized_tool_call_to_oci(tc) for tc in message["tool_calls"]]
+ if "tool_call_id" in message:
+ encoded["toolCallId"] = message["tool_call_id"]
+ return encoded
+
+ @staticmethod
+ def _model_from_envelope(envelope: typing.Mapping[str, typing.Any] | None) -> str | None:
+ if not envelope:
+ return None
+ serving_mode = _get_first(envelope, "servingMode")
+ if not isinstance(serving_mode, dict):
+ return None
+ model = _get_first(serving_mode, "modelId") or _get_first(serving_mode, "endpointId")
+ return str(model) if model is not None else None
+
+ @staticmethod
+ def _decode_generic(
+ chat_request: typing.Mapping[str, typing.Any],
+ ) -> tuple[list[dict[str, typing.Any]], dict[str, typing.Any]]:
+ messages: list[dict[str, typing.Any]] = []
+ for message in _get_first(chat_request, "messages") or []:
+ role = str(_get_first(message, "role") or "USER").lower()
+ normalized: dict[str, typing.Any] = {
+ "role": role,
+ "content": _generic_content_to_text(_get_first(message, "content")),
+ }
+ tool_calls = _get_first(message, "toolCalls")
+ if tool_calls is not None:
+ normalized["tool_calls"] = [_oci_tool_call_to_normalized(tc) for tc in tool_calls]
+ tool_call_id = _get_first(message, "toolCallId")
+ if tool_call_id is not None:
+ normalized["tool_call_id"] = tool_call_id
+ messages.append(normalized)
+
+ params = {
+ normalized_key: chat_request[oci_key]
+ for oci_key, normalized_key in _GENERIC_PARAM_MAP.items()
+ if oci_key in chat_request
+ }
+ return messages, params
+
+ @staticmethod
+ def _decode_cohere(
+ chat_request: typing.Mapping[str, typing.Any],
+ ) -> tuple[list[dict[str, typing.Any]], dict[str, typing.Any]]:
+ messages: list[dict[str, typing.Any]] = []
+
+ preamble = _get_first(chat_request, "preambleOverride")
+ if preamble:
+ messages.append({"role": "system", "content": preamble})
+
+ for turn in _get_first(chat_request, "chatHistory") or []:
+ role = _COHERE_ROLE_TO_NORMALIZED.get(str(_get_first(turn, "role") or "USER").upper(), "user")
+ messages.append({"role": role, "content": _get_first(turn, "message")})
+
+ current = _get_first(chat_request, "message")
+ if current is not None:
+ messages.append({"role": "user", "content": current})
+
+ params = {
+ normalized_key: chat_request[oci_key]
+ for oci_key, normalized_key in _COHERE_PARAM_MAP.items()
+ if oci_key in chat_request
+ }
+ return messages, params
+
+ @staticmethod
+ def _encode_cohere(chat_request: dict[str, typing.Any], annotated: AnnotatedLLMRequest) -> None:
+ messages = list(annotated.messages)
+
+ if messages and str(messages[0].get("role")) == "system":
+ chat_request["preambleOverride"] = messages[0].get("content")
+ messages = messages[1:]
+
+ current_message = ""
+ if messages and str(messages[-1].get("role")) == "user":
+ current_message = messages[-1].get("content") or ""
+ messages = messages[:-1]
+
+ history = [
+ {
+ "role": _NORMALIZED_ROLE_TO_COHERE.get(str(turn.get("role")), "USER"),
+ "message": turn.get("content"),
+ }
+ for turn in messages
+ ]
+
+ chat_request["message"] = current_message
+ if history or "chatHistory" in chat_request:
+ chat_request["chatHistory"] = history
+
+
+class OCIGenAIResponseCodec:
+ """Response codec for OCI Generative AI ``ChatResult`` payloads.
+
+ Normalizes both ``GENERIC`` (``choices``-based) and ``COHERE``
+ (``text``-based) chat responses for ``LLMEnd`` event annotation. Accepts
+ the full ``ChatResult`` (``modelId``, ``chatResponse``) or a bare chat
+ response object.
+ """
+
+ def decode_response(self, response: Json) -> AnnotatedLLMResponse:
+ """Decode a raw OCI chat response into ``AnnotatedLLMResponse``."""
+ if not isinstance(response, dict):
+ return AnnotatedLLMResponse(extra={"raw": response})
+
+ chat_response = _get_first(response, "chatResponse")
+ envelope = response if isinstance(chat_response, dict) else None
+ chat_response = dict(chat_response) if isinstance(chat_response, dict) else dict(response)
+
+ model = None
+ if envelope is not None:
+ model_value = _get_first(envelope, "modelId")
+ model = str(model_value) if model_value is not None else None
+
+ api_format = str(_get_first(chat_response, "apiFormat") or "GENERIC").upper()
+
+ message: typing.Any = None
+ tool_calls: typing.Any = None
+ finish_reason: typing.Any = None
+
+ if api_format == "COHERE":
+ message = _get_first(chat_response, "text")
+ tool_calls = _get_first(chat_response, "toolCalls")
+ finish_reason = _get_first(chat_response, "finishReason")
+ else:
+ choices = _get_first(chat_response, "choices") or []
+ if choices:
+ first_choice = choices[0]
+ finish_reason = _get_first(first_choice, "finishReason")
+ raw_message = _get_first(first_choice, "message")
+ if isinstance(raw_message, dict):
+ message = _generic_content_to_text(_get_first(raw_message, "content"))
+ tool_calls = _get_first(raw_message, "toolCalls")
+
+ usage = _normalize_usage(_get_first(chat_response, "usage"))
+
+ return AnnotatedLLMResponse(
+ model=model,
+ message=message,
+ tool_calls=tool_calls if isinstance(tool_calls, list) else None,
+ finish_reason=finish_reason,
+ usage=usage,
+ api_specific={"api": "custom", "api_name": "oci_genai", "data": {"apiFormat": api_format}},
+ )
+
+
+__all__ = [
+ "OCIGenAIChatCodec",
+ "OCIGenAIResponseCodec",
+]
diff --git a/python/tests/integrations/langchain_tests/test_oci_genai.py b/python/tests/integrations/langchain_tests/test_oci_genai.py
new file mode 100644
index 000000000..5ee36eb16
--- /dev/null
+++ b/python/tests/integrations/langchain_tests/test_oci_genai.py
@@ -0,0 +1,145 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Tests for NeMo Relay observability over OCI Generative AI (langchain-oci) payloads."""
+
+from __future__ import annotations
+
+import types
+import typing
+from unittest.mock import MagicMock
+from uuid import uuid4
+
+import pytest
+
+if typing.TYPE_CHECKING:
+ from nemo_relay.integrations.langchain.callbacks import NemoRelayCallbackHandler
+
+
+OCI_CHAT_MODEL_ID = ["langchain_oci", "chat_models", "oci_generative_ai", "ChatOCIGenAI"]
+
+
+def _make_mock_nemo_relay() -> MagicMock:
+ """Build a minimal mock of the ``nemo_relay`` module."""
+ mock_nemo_relay = MagicMock(name="nemo_relay")
+ mock_nemo_relay.ScopeType = types.SimpleNamespace(Agent="Agent")
+
+ scope = types.SimpleNamespace()
+ scope.push = MagicMock(
+ side_effect=lambda name, scope_type, **kwargs: types.SimpleNamespace(
+ uuid=str(uuid4()),
+ name=name,
+ scope_type=scope_type,
+ kwargs=kwargs,
+ )
+ )
+ scope.pop = MagicMock()
+ mock_nemo_relay.scope = scope
+ return mock_nemo_relay
+
+
+@pytest.fixture(name="callbacks_module", scope="session")
+def callbacks_module_fixture() -> types.ModuleType:
+ """Fixture to provide the callbacks module."""
+ import nemo_relay.integrations.langchain.callbacks as callbacks_module
+
+ return callbacks_module
+
+
+@pytest.fixture()
+def mock_nemo_relay(monkeypatch: pytest.MonkeyPatch, callbacks_module: types.ModuleType) -> MagicMock:
+ mock_nemo_relay = _make_mock_nemo_relay()
+ monkeypatch.setattr(callbacks_module, "nemo_relay", mock_nemo_relay)
+ return mock_nemo_relay
+
+
+@pytest.fixture()
+def handler(mock_nemo_relay: MagicMock) -> NemoRelayCallbackHandler:
+ from nemo_relay.integrations.langchain.callbacks import NemoRelayCallbackHandler
+
+ return NemoRelayCallbackHandler()
+
+
+class TestOciGenAiScopeNaming:
+ """Verify OCI Generative AI chain runs resolve to a usable scope name."""
+
+ def test_scope_named_from_langchain_oci_id_list(
+ self, handler: NemoRelayCallbackHandler, mock_nemo_relay: MagicMock
+ ):
+ """A langchain-oci serialized payload without a name falls back to the class name."""
+ run_id = uuid4()
+
+ handler.on_chain_start(
+ {"id": OCI_CHAT_MODEL_ID},
+ {"input": "What is the weather in San Francisco?"},
+ run_id=run_id,
+ )
+
+ mock_nemo_relay.scope.push.assert_called_once()
+ args, _ = mock_nemo_relay.scope.push.call_args
+ assert args == ("ChatOCIGenAI", mock_nemo_relay.ScopeType.Agent)
+
+ def test_oci_model_metadata_passes_through(
+ self, handler: NemoRelayCallbackHandler, mock_nemo_relay: MagicMock
+ ):
+ """OCI-specific metadata (model id, serving mode) survives into the scope."""
+ run_id = uuid4()
+ metadata = {
+ "ls_provider": "oci_generative_ai",
+ "ls_model_name": "meta.llama-3.3-70b-instruct",
+ "serving_mode": "ON_DEMAND",
+ }
+
+ handler.on_chain_start(
+ {"id": OCI_CHAT_MODEL_ID},
+ {"input": "test"},
+ run_id=run_id,
+ metadata=metadata,
+ )
+
+ _, kwargs = mock_nemo_relay.scope.push.call_args
+ assert kwargs["metadata"]["ls_provider"] == "oci_generative_ai"
+ assert kwargs["metadata"]["ls_model_name"] == "meta.llama-3.3-70b-instruct"
+ assert kwargs["metadata"]["serving_mode"] == "ON_DEMAND"
+ assert kwargs["metadata"]["langchain_run_id"] == str(run_id)
+
+ def test_dedicated_endpoint_model_id_in_metadata(
+ self, handler: NemoRelayCallbackHandler, mock_nemo_relay: MagicMock
+ ):
+ """Dedicated AI cluster endpoints (endpoint OCID model ids) are preserved."""
+ run_id = uuid4()
+ endpoint_ocid = "ocid1.generativeaiendpoint.oc1.us-chicago-1.example"
+
+ handler.on_chain_start(
+ {"id": OCI_CHAT_MODEL_ID},
+ {"input": "test"},
+ run_id=run_id,
+ metadata={"ls_model_name": endpoint_ocid, "serving_mode": "DEDICATED"},
+ )
+
+ _, kwargs = mock_nemo_relay.scope.push.call_args
+ assert kwargs["metadata"]["ls_model_name"] == endpoint_ocid
+ assert kwargs["metadata"]["serving_mode"] == "DEDICATED"
+
+
+class TestOciGenAiScopeLifecycle:
+ """Verify start/end/error pair correctly for OCI-backed chain runs."""
+
+ def test_end_pops_scope(self, handler: NemoRelayCallbackHandler, mock_nemo_relay: MagicMock):
+ run_id = uuid4()
+ handler.on_chain_start({"id": OCI_CHAT_MODEL_ID}, {"input": "test"}, run_id=run_id)
+ handler.on_chain_end({"output": "72 degrees"}, run_id=run_id)
+
+ mock_nemo_relay.scope.pop.assert_called_once()
+
+ def test_error_pops_scope_with_error_status(
+ self, handler: NemoRelayCallbackHandler, mock_nemo_relay: MagicMock
+ ):
+ run_id = uuid4()
+ handler.on_chain_start({"id": OCI_CHAT_MODEL_ID}, {"input": "test"}, run_id=run_id)
+ handler.on_chain_error(RuntimeError("OCI service error: 429"), run_id=run_id)
+
+ mock_nemo_relay.scope.pop.assert_called_once()
+ _, kwargs = mock_nemo_relay.scope.pop.call_args
+ metadata = kwargs.get("metadata") or {}
+ assert metadata.get("otel.status_code") == "ERROR"
diff --git a/python/tests/providers/test_oci_genai_codec.py b/python/tests/providers/test_oci_genai_codec.py
new file mode 100644
index 000000000..101bf01f5
--- /dev/null
+++ b/python/tests/providers/test_oci_genai_codec.py
@@ -0,0 +1,318 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Unit tests for the OCI Generative AI request and response codecs."""
+
+from __future__ import annotations
+
+import typing
+
+from nemo_relay import LLMRequest
+from nemo_relay.providers.oci_genai import OCIGenAIChatCodec, OCIGenAIResponseCodec
+
+DEDICATED_ENDPOINT = "ocid1.generativeaiendpoint.oc1.us-chicago-1.example"
+
+
+def _j(value: object) -> typing.Any:
+ """Erase JSON-union typing for assertion subscripting."""
+ return value
+
+
+GENERIC_CHAT_DETAILS: dict[str, typing.Any] = {
+ "compartmentId": "ocid1.compartment.oc1..example",
+ "servingMode": {"servingType": "DEDICATED", "endpointId": DEDICATED_ENDPOINT},
+ "chatRequest": {
+ "apiFormat": "GENERIC",
+ "messages": [
+ {"role": "SYSTEM", "content": [{"type": "TEXT", "text": "You are terse."}]},
+ {"role": "USER", "content": [{"type": "TEXT", "text": "My SSN is 111-22-3333."}]},
+ ],
+ "maxTokens": 600,
+ "temperature": 0.0,
+ },
+}
+
+COHERE_CHAT_DETAILS: dict[str, typing.Any] = {
+ "compartmentId": "ocid1.compartment.oc1..example",
+ "servingMode": {"servingType": "ON_DEMAND", "modelId": "cohere.command-a-03-2025"},
+ "chatRequest": {
+ "apiFormat": "COHERE",
+ "preambleOverride": "You are terse.",
+ "chatHistory": [
+ {"role": "USER", "message": "hello"},
+ {"role": "CHATBOT", "message": "hi"},
+ ],
+ "message": "What is the weather?",
+ "maxTokens": 100,
+ },
+}
+
+# Shape observed from a live dedicated-endpoint chat (imported NVIDIA Nemotron 3).
+GENERIC_CHAT_RESULT: dict[str, typing.Any] = {
+ "modelId": DEDICATED_ENDPOINT,
+ "modelVersion": "1.0",
+ "chatResponse": {
+ "apiFormat": "GENERIC",
+ "timeCreated": "2026-07-23T22:59:00.000Z",
+ "choices": [
+ {
+ "index": 0,
+ "message": {
+ "role": "ASSISTANT",
+ "content": [{"type": "TEXT", "text": "NEMOTRON3_OK"}],
+ },
+ "finishReason": "stop",
+ }
+ ],
+ "usage": {"promptTokens": 18, "completionTokens": 5, "totalTokens": 23},
+ },
+}
+
+COHERE_CHAT_RESULT: dict[str, typing.Any] = {
+ "modelId": "cohere.command-a-03-2025",
+ "chatResponse": {
+ "apiFormat": "COHERE",
+ "text": "Sunny and 72.",
+ "finishReason": "COMPLETE",
+ "usage": {"promptTokens": 12, "completionTokens": 4, "totalTokens": 16},
+ },
+}
+
+
+class TestGenericRequestCodec:
+ def test_decode_envelope(self):
+ annotated = OCIGenAIChatCodec().decode(LLMRequest({}, GENERIC_CHAT_DETAILS))
+
+ assert [m["role"] for m in annotated.messages] == ["system", "user"]
+ assert _j(annotated.messages[1])["content"] == "My SSN is 111-22-3333."
+ assert annotated.model == DEDICATED_ENDPOINT
+ assert _j(annotated.params)["max_tokens"] == 600
+ assert _j(annotated.params)["temperature"] == 0.0
+ assert _j(annotated.api_specific)["api_name"] == "oci_genai"
+ assert _j(annotated.api_specific)["data"]["apiFormat"] == "GENERIC"
+
+ def test_decode_bare_chat_request(self):
+ annotated = OCIGenAIChatCodec().decode(LLMRequest({}, GENERIC_CHAT_DETAILS["chatRequest"]))
+
+ assert [m["role"] for m in annotated.messages] == ["system", "user"]
+ assert annotated.model is None
+
+ def test_redaction_round_trip_preserves_envelope(self):
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, GENERIC_CHAT_DETAILS)
+ annotated = codec.decode(original)
+
+ edited_messages = [dict(m) for m in annotated.messages]
+ edited_messages[1]["content"] = "My SSN is [REDACTED]."
+ from nemo_relay._native import AnnotatedLLMRequest
+
+ edited = AnnotatedLLMRequest(
+ edited_messages,
+ model=annotated.model,
+ params=annotated.params,
+ api_specific=annotated.api_specific,
+ extra=annotated.extra,
+ )
+
+ encoded = codec.encode(edited, original)
+
+ chat_request = _j(encoded.content)["chatRequest"]
+ assert chat_request["messages"][1]["role"] == "USER"
+ assert chat_request["messages"][1]["content"] == [{"type": "TEXT", "text": "My SSN is [REDACTED]."}]
+ # Envelope fields survive untouched.
+ assert _j(encoded.content)["compartmentId"] == GENERIC_CHAT_DETAILS["compartmentId"]
+ assert _j(encoded.content)["servingMode"] == GENERIC_CHAT_DETAILS["servingMode"]
+ assert chat_request["maxTokens"] == 600
+
+ def test_tool_calls_round_trip(self):
+ payload: dict[str, typing.Any] = {
+ "apiFormat": "GENERIC",
+ "messages": [
+ {
+ "role": "ASSISTANT",
+ "content": [],
+ "toolCalls": [{"id": "call-1", "type": "FUNCTION", "name": "get_weather", "arguments": "{}"}],
+ },
+ {"role": "TOOL", "content": [{"type": "TEXT", "text": "72F"}], "toolCallId": "call-1"},
+ ],
+ }
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, payload)
+ annotated = codec.decode(original)
+
+ assert _j(annotated.messages[0])["tool_calls"][0]["function"]["name"] == "get_weather"
+ assert _j(annotated.messages[1])["tool_call_id"] == "call-1"
+
+ encoded = codec.encode(annotated, original)
+ assert _j(encoded.content)["messages"][0]["toolCalls"][0] == {
+ "id": "call-1",
+ "type": "FUNCTION",
+ "name": "get_weather",
+ "arguments": "{}",
+ }
+ assert _j(encoded.content)["messages"][1]["toolCallId"] == "call-1"
+
+
+class TestCohereRequestCodec:
+ def test_decode(self):
+ annotated = OCIGenAIChatCodec().decode(LLMRequest({}, COHERE_CHAT_DETAILS))
+
+ assert [m["role"] for m in annotated.messages] == ["system", "user", "assistant", "user"]
+ assert _j(annotated.messages[0])["content"] == "You are terse."
+ assert _j(annotated.messages[-1])["content"] == "What is the weather?"
+ assert annotated.model == "cohere.command-a-03-2025"
+ assert _j(annotated.api_specific)["data"]["apiFormat"] == "COHERE"
+
+ def test_round_trip(self):
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, COHERE_CHAT_DETAILS)
+ annotated = codec.decode(original)
+ encoded = codec.encode(annotated, original)
+
+ chat_request = _j(encoded.content)["chatRequest"]
+ assert chat_request["message"] == "What is the weather?"
+ assert chat_request["preambleOverride"] == "You are terse."
+ assert chat_request["chatHistory"] == [
+ {"role": "USER", "message": "hello"},
+ {"role": "CHATBOT", "message": "hi"},
+ ]
+ assert _j(encoded.content)["servingMode"] == COHERE_CHAT_DETAILS["servingMode"]
+
+
+class TestIdentityInvariant:
+ """encode(decode(original), original) == original at the JSON-value level.
+
+ This is the same guarantee the built-in provider codecs document in the
+ Provider Codecs guide, including preservation of unmodeled fields.
+ """
+
+ UNMODELED_GENERIC: typing.ClassVar[dict[str, typing.Any]] = {
+ "compartmentId": "ocid1.compartment.oc1..example",
+ "opcRetryToken": "retry-abc",
+ "servingMode": {"servingType": "DEDICATED", "endpointId": DEDICATED_ENDPOINT, "futureFlag": True},
+ "chatRequest": {
+ "apiFormat": "GENERIC",
+ "messages": [
+ {"role": "SYSTEM", "content": [{"type": "TEXT", "text": "Be terse."}], "name": "sys-1"},
+ {"role": "USER", "content": [{"type": "TEXT", "text": "hello"}], "unknownPerMessage": 7},
+ ],
+ "maxTokens": 64,
+ "topK": 40,
+ "seed": 7,
+ "unknownFutureField": {"nested": True},
+ },
+ }
+
+ def test_generic_identity(self):
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, self.UNMODELED_GENERIC)
+ encoded = codec.encode(codec.decode(original), original)
+
+ assert dict(encoded.content) == self.UNMODELED_GENERIC
+
+ def test_cohere_identity(self):
+ cohere_payload = dict(COHERE_CHAT_DETAILS)
+ cohere_payload["chatRequest"] = {**COHERE_CHAT_DETAILS["chatRequest"], "isForceSingleStep": True}
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, cohere_payload)
+ encoded = codec.encode(codec.decode(original), original)
+
+ assert dict(encoded.content) == cohere_payload
+
+ def test_edit_preserves_unmodeled_fields_on_untouched_messages(self):
+ """Editing one message must not disturb unmodeled fields on the others."""
+ from nemo_relay._native import AnnotatedLLMRequest
+
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, self.UNMODELED_GENERIC)
+ annotated = codec.decode(original)
+
+ edited_messages = [dict(m) for m in annotated.messages]
+ edited_messages[1]["content"] = "redacted"
+ edited = AnnotatedLLMRequest(
+ edited_messages,
+ model=annotated.model,
+ params=annotated.params,
+ api_specific=annotated.api_specific,
+ extra=annotated.extra,
+ )
+
+ encoded = codec.encode(edited, original)
+ chat_request = _j(encoded.content)["chatRequest"]
+
+ # Untouched system message keeps its unmodeled per-message field.
+ assert chat_request["messages"][0] == self.UNMODELED_GENERIC["chatRequest"]["messages"][0]
+ # Edited message carries the redaction.
+ assert chat_request["messages"][1]["content"] == [{"type": "TEXT", "text": "redacted"}]
+ # Unmodeled request-level fields survive.
+ assert chat_request["topK"] == 40
+ assert chat_request["seed"] == 7
+ assert chat_request["unknownFutureField"] == {"nested": True}
+ assert encoded.content["opcRetryToken"] == "retry-abc"
+
+ def test_param_edit_only_touches_changed_param(self):
+ from nemo_relay._native import AnnotatedLLMRequest
+
+ codec = OCIGenAIChatCodec()
+ original = LLMRequest({}, self.UNMODELED_GENERIC)
+ annotated = codec.decode(original)
+
+ params = dict(annotated.params or {})
+ params["max_tokens"] = 128
+ edited = AnnotatedLLMRequest(
+ [dict(m) for m in annotated.messages],
+ model=annotated.model,
+ params=params,
+ api_specific=annotated.api_specific,
+ extra=annotated.extra,
+ )
+
+ encoded = codec.encode(edited, original)
+ chat_request = _j(encoded.content)["chatRequest"]
+
+ assert chat_request["maxTokens"] == 128
+ assert chat_request["messages"] == self.UNMODELED_GENERIC["chatRequest"]["messages"]
+
+
+class TestResponseCodec:
+ def test_generic_chat_result(self):
+ annotated = OCIGenAIResponseCodec().decode_response(GENERIC_CHAT_RESULT)
+
+ assert annotated.model == DEDICATED_ENDPOINT
+ assert annotated.message == "NEMOTRON3_OK"
+ assert annotated.finish_reason == "stop"
+ assert _j(annotated.usage)["total_tokens"] == 23
+ assert _j(annotated.usage)["prompt_tokens"] == 18
+ assert _j(annotated.api_specific)["data"]["apiFormat"] == "GENERIC"
+
+ def test_cohere_chat_result(self):
+ annotated = OCIGenAIResponseCodec().decode_response(COHERE_CHAT_RESULT)
+
+ assert annotated.message == "Sunny and 72."
+ assert annotated.finish_reason == "COMPLETE"
+ assert annotated.model == "cohere.command-a-03-2025"
+ assert _j(annotated.api_specific)["data"]["apiFormat"] == "COHERE"
+
+ def test_kebab_case_cli_shape(self):
+ cli_shaped = {
+ "model-id": DEDICATED_ENDPOINT,
+ "chat-response": {
+ "api-format": "GENERIC",
+ "choices": [
+ {
+ "message": {"role": "ASSISTANT", "content": [{"type": "TEXT", "text": "hello"}]},
+ "finish-reason": "stop",
+ }
+ ],
+ "usage": {"total-tokens": 9},
+ },
+ }
+ annotated = OCIGenAIResponseCodec().decode_response(cli_shaped)
+
+ assert annotated.message == "hello"
+ assert annotated.finish_reason == "stop"
+
+ def test_non_dict_response(self):
+ annotated = OCIGenAIResponseCodec().decode_response("plain text")
+
+ assert _j(annotated.extra)["raw"] == "plain text"
diff --git a/python/tests/providers/test_oci_genai_live.py b/python/tests/providers/test_oci_genai_live.py
new file mode 100644
index 000000000..3d85bde04
--- /dev/null
+++ b/python/tests/providers/test_oci_genai_live.py
@@ -0,0 +1,125 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Live integration tests for the OCI Generative AI codecs.
+
+These tests execute a real chat call through ``nemo_relay.llm.execute`` with
+``OCIGenAIChatCodec`` and ``OCIGenAIResponseCodec`` against an OCI Generative
+AI endpoint. They are skipped unless the environment opts in:
+
+- ``NEMO_RELAY_OCI_LIVE=1``
+- ``OCI_GENAI_COMPARTMENT_ID``: compartment OCID for the request.
+- ``OCI_GENAI_ENDPOINT_ID`` (dedicated AI cluster endpoint OCID) or
+ ``OCI_GENAI_MODEL_ID`` (on-demand model id such as
+ ``meta.llama-3.3-70b-instruct``).
+- ``OCI_CLI_PROFILE`` (default ``DEFAULT``) and optional
+ ``OCI_GENAI_REGION`` (default ``us-chicago-1``).
+
+Example::
+
+ NEMO_RELAY_OCI_LIVE=1 \
+ OCI_GENAI_COMPARTMENT_ID=ocid1.compartment.oc1..example \
+ OCI_GENAI_ENDPOINT_ID=ocid1.generativeaiendpoint.oc1.us-chicago-1.example \
+ OCI_CLI_PROFILE=DEFAULT \
+ uv run --with oci pytest python/tests/providers/test_oci_genai_live.py
+"""
+
+from __future__ import annotations
+
+import os
+import typing
+
+import pytest
+
+pytestmark = pytest.mark.skipif(
+ os.environ.get("NEMO_RELAY_OCI_LIVE") != "1",
+ reason="live OCI Generative AI tests require NEMO_RELAY_OCI_LIVE=1 and OCI credentials",
+)
+
+
+def _signer_and_endpoint():
+ """Build a request signer and the regional chat endpoint URL.
+
+ Signing raw HTTP (instead of using SDK model classes) sends the codec's
+ encoded payload to the service verbatim, so the test also validates that
+ ``OCIGenAIChatCodec.encode()`` produces the exact OCI wire format.
+ """
+ oci = pytest.importorskip("oci")
+
+ profile = os.environ.get("OCI_CLI_PROFILE", "DEFAULT")
+ region = os.environ.get("OCI_GENAI_REGION", "us-chicago-1")
+ config = oci.config.from_file(profile_name=profile)
+
+ token_file = config.get("security_token_file")
+ if token_file:
+ with open(os.path.expanduser(token_file), encoding="utf-8") as handle:
+ token = handle.read().strip()
+ private_key = oci.signer.load_private_key_from_file(config["key_file"])
+ signer = oci.auth.signers.SecurityTokenSigner(token, private_key)
+ else:
+ signer = oci.signer.Signer(
+ tenancy=config["tenancy"],
+ user=config["user"],
+ fingerprint=config["fingerprint"],
+ private_key_file_location=config["key_file"],
+ pass_phrase=config.get("pass_phrase"),
+ )
+
+ endpoint = f"https://inference.generativeai.{region}.oci.oraclecloud.com/20231130/actions/chat"
+ return signer, endpoint
+
+
+def _serving_mode() -> dict[str, str]:
+ endpoint_id = os.environ.get("OCI_GENAI_ENDPOINT_ID")
+ if endpoint_id:
+ return {"servingType": "DEDICATED", "endpointId": endpoint_id}
+ model_id = os.environ.get("OCI_GENAI_MODEL_ID")
+ if not model_id:
+ pytest.skip("set OCI_GENAI_ENDPOINT_ID or OCI_GENAI_MODEL_ID")
+ return {"servingType": "ON_DEMAND", "modelId": model_id}
+
+
+async def test_generic_chat_through_relay_with_codecs():
+ import nemo_relay
+ from nemo_relay.providers.oci_genai import OCIGenAIChatCodec, OCIGenAIResponseCodec
+
+ requests = pytest.importorskip("requests")
+ signer, endpoint = _signer_and_endpoint()
+ compartment_id = os.environ["OCI_GENAI_COMPARTMENT_ID"]
+
+ chat_details: dict[str, typing.Any] = {
+ "compartmentId": compartment_id,
+ "servingMode": _serving_mode(),
+ "chatRequest": {
+ "apiFormat": "GENERIC",
+ "messages": [
+ {
+ "role": "USER",
+ "content": [{"type": "TEXT", "text": "/no_think Reply with exactly: RELAY_OCI_OK"}],
+ }
+ ],
+ "maxTokens": 600,
+ "temperature": 0.0,
+ },
+ }
+
+ async def call_oci(request: nemo_relay.LLMRequest):
+ response = requests.post(endpoint, json=request.content, auth=signer, timeout=120)
+ response.raise_for_status()
+ return response.json()
+
+ result = await nemo_relay.llm.execute(
+ "oci-genai",
+ nemo_relay.LLMRequest({}, chat_details),
+ call_oci,
+ codec=OCIGenAIChatCodec(),
+ response_codec=OCIGenAIResponseCodec(),
+ )
+
+ text = result["chatResponse"]["choices"][0]["message"]["content"][0]["text"]
+ assert "RELAY_OCI_OK" in text
+
+ # The response codec must decode the same payload without error.
+ annotated = OCIGenAIResponseCodec().decode_response(result)
+ assert annotated.message is not None
+ assert "RELAY_OCI_OK" in str(annotated.message)
diff --git a/uv.lock b/uv.lock
index 5e6261431..f4a156d29 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,8 +2,10 @@ version = 1
revision = 3
requires-python = ">=3.11"
resolution-markers = [
- "sys_platform == 'linux'",
- "sys_platform != 'linux'",
+ "python_full_version >= '3.12' and sys_platform == 'linux'",
+ "python_full_version < '3.12' and sys_platform == 'linux'",
+ "python_full_version >= '3.12' and sys_platform != 'linux'",
+ "python_full_version < '3.12' and sys_platform != 'linux'",
]
[manifest]
@@ -410,6 +412,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958, upload-time = "2026-04-02T09:28:37.794Z" },
]
+[[package]]
+name = "circuitbreaker"
+version = "2.1.3"
+source = { registry = "https://pypi.org/simple" }
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