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Szymon Nastaly 2026-10-14 10:49:13 -05:00 • committed by GitHub
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21 changed files with 405 additions and 15 deletions

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@ -33,6 +33,18 @@ Configure Strix using environment variables or a config file.
the LiteLLM and native OpenAI routing paths.
</ParamField>
<ParamField path="LLM_EXTRA_BODY" type="string">
Extra fields merged into the JSON body of every request to the main model, as a JSON object
(e.g. `{"service_tier":"flex","safety_identifier":"9f86d081..."}`). Use it
for provider parameters Strix doesn't expose, such as OpenAI's `service_tier`
or `safety_identifier`. Applies to both the LiteLLM and native OpenAI routing
paths. Fields are not validated, so the provider or gateway must accept them;
unknown fields are usually rejected. Extra fields can override fields Strix
sends and break requests, so don't set request-shaping fields such as `model`, `messages`, `input`,
`tools` or `stream`, and use `STRIX_REASONING_EFFORT` rather than
`reasoning_effort`.
</ParamField>
<ParamField path="LLM_TIMEOUT" default="300" type="integer">
Request timeout in seconds for LLM calls.
</ParamField>
@ -88,6 +100,12 @@ affecting the agents that do the actual testing.
inherits `LLM_EXTRA_HEADERS`; set this when its endpoint needs custom headers.
</ParamField>
<ParamField path="DEDUPE_LLM_EXTRA_BODY" type="string">
Optional JSON object of extra request-body fields sent on every
deduplication-model request. A dedicated dedupe model never inherits
`LLM_EXTRA_BODY`; set this when its endpoint needs them too.
</ParamField>
<ParamField path="STRIX_DEDUPE_REASONING_EFFORT" type="string">
Reasoning effort for the deduplication model. Defaults to the model's own
baseline when unset.

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@ -68,6 +68,21 @@ export LLM_API_KEY="your-bearer-token" # sent as Authorization: Bearer
export LLM_EXTRA_HEADERS='{"X-Feature-Key":"value","X-Tenant":"acme"}'
```
### Extra request-body fields
Some provider parameters go in the request body rather than a header, and
Strix has no dedicated setting for them. Set them with `LLM_EXTRA_BODY` as a
JSON object — they are merged into the body of every request to the model. For example, to
use OpenAI's cheaper flex processing and attribute requests to a user with a
safety identifier (a hash, never a raw email):
```bash
export LLM_EXTRA_BODY='{"service_tier":"flex","safety_identifier":"9f86d081884c7d65"}'
```
Fields aren't validated, so use only fields your provider accepts, and don't
set fields Strix already sends, such as `model`, `messages` or `stream`.
For endpoints behind a private CA, point Strix at your certificate bundle with
the standard `SSL_CERT_FILE=/path/to/ca-bundle.pem` — never disable TLS
verification against a real endpoint.

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@ -6,7 +6,8 @@ import json
import logging
import os
from pathlib import Path
from typing import TYPE_CHECKING, Any
from types import UnionType
from typing import TYPE_CHECKING, Any, Union, get_args, get_origin
from pydantic import AliasChoices, BaseModel
@ -126,13 +127,36 @@ def _read_json_overrides(path: Path) -> dict[str, dict[str, Any]]:
continue # env wins under some alias; skip the JSON file for this field
for alias in aliases:
if alias in env_block_upper:
sub_data[fname] = env_block_upper[alias]
sub_data[fname] = _decode_saved_value(finfo, env_block_upper[alias])
break
if sub_data:
nested[sub_name] = sub_data
return nested
def _decode_saved_value(finfo: FieldInfo, value: Any) -> Any:
"""Decode a JSON string saved for a dict field, as pydantic does for env vars.
``persist_current`` saves env values verbatim, so a dict setting such as
``LLM_EXTRA_HEADERS`` lands in the file as JSON text. Values that are
already objects, or aren't valid JSON, are left for validation to judge.
"""
if not isinstance(value, str) or not _is_dict_field(finfo):
return value
try:
return json.loads(value)
except ValueError:
return value
def _is_dict_field(finfo: FieldInfo) -> bool:
annotation = finfo.annotation
candidates = (
get_args(annotation) if get_origin(annotation) in (Union, UnionType) else (annotation,)
)
return any(c is dict or get_origin(c) is dict for c in candidates)
def _first_alias_value(aliases: list[str], source: Mapping[str, Any]) -> Any | None:
return next((source[alias] for alias in aliases if alias in source), None)

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@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Literal
from typing import Any, Literal
from pydantic import AliasChoices, Field, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
@ -55,6 +55,11 @@ class LlmSettings(BaseSettings):
alias="LLM_EXTRA_HEADERS",
repr=False,
)
extra_body: dict[str, Any] | None = Field(
default=None,
alias="LLM_EXTRA_BODY",
repr=False,
)
reasoning_effort: ReasoningEffort = Field(default="high", alias="STRIX_REASONING_EFFORT")
force_required_tool_choice: bool = Field(
default=False,
@ -110,6 +115,11 @@ class DedupeSettings(BaseSettings):
alias="DEDUPE_LLM_EXTRA_HEADERS",
repr=False,
)
extra_body: dict[str, Any] | None = Field(
default=None,
alias="DEDUPE_LLM_EXTRA_BODY",
repr=False,
)
@field_validator("reasoning_effort", mode="before")
@classmethod

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@ -250,6 +250,7 @@ def make_model_settings(
request_timeout: float | None = None,
prompt_cache: bool = True,
extra_headers: dict[str, str] | None = None,
extra_body: dict[str, Any] | None = None,
has_tools: bool = True,
) -> ModelSettings:
headers = _request_headers(model_name, extra_headers)
@ -259,13 +260,16 @@ def make_model_settings(
include_usage=True,
extra_args=request_timeout_extra_args(request_timeout),
extra_headers=headers,
extra_body=dict(extra_body) if extra_body else None,
)
if (
reasoning_effort is not None
and reasoning_effort != "none"
and model_supports_reasoning(model_name)
):
model_settings = model_settings.resolve(_reasoning_settings(reasoning_effort))
model_settings = model_settings.resolve(
_reasoning_settings(reasoning_effort, extra_body),
)
if force_required_tool_choice and _accepts_required_tool_choice(model_name):
model_settings = model_settings.resolve(ModelSettings(tool_choice="required"))
@ -290,7 +294,9 @@ def _request_headers(
return headers or None
def _reasoning_settings(effort: ReasoningEffort) -> ModelSettings:
def _reasoning_settings(
effort: ReasoningEffort, extra_body: dict[str, Any] | None = None
) -> ModelSettings:
"""``max`` is not in the OpenAI SDK's ``Reasoning.effort`` enum, so send it as
a raw body field instead — also keeping it clear of LiteLLM's DeepSeek mapping,
which collapses every ``reasoning_effort`` level to plain thinking-enabled.
@ -298,11 +304,13 @@ def _reasoning_settings(effort: ReasoningEffort) -> ModelSettings:
It goes in ``extra_body``, the field every model implementation forwards as the
request's ``extra_body``; the same value under ``extra_args`` collides with that
keyword and raises before a request is ever sent.
keyword and raises before a request is ever sent. ``resolve`` replaces
``extra_body`` wholesale, so the user's ``LLM_EXTRA_BODY`` fields are carried
over, with ``reasoning_effort`` taking precedence.
"""
if effort != "max":
return ModelSettings(reasoning=Reasoning(effort=effort))
return ModelSettings(extra_body={"reasoning_effort": "max"})
return ModelSettings(extra_body={**(extra_body or {}), "reasoning_effort": "max"})
def _prompt_cache_extra_args(model_name: str) -> dict[str, Any] | None:

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@ -401,6 +401,7 @@ async def run_strix_scan(
request_timeout=settings.llm.timeout,
prompt_cache=settings.llm.prompt_cache,
extra_headers=settings.llm.extra_headers,
extra_body=settings.llm.extra_body,
)
run_config = RunConfig(
model=resolved_model,

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@ -164,12 +164,13 @@ async def warm_up_llm() -> None:
raw_model = dedupe_model
deduper = resolve_dedupe_model(settings.dedupe, dedupe_model)
# A dedicated dedupe model may route to another provider, which must
# never receive the main endpoint's headers; it has its own
# DEDUPE_LLM_EXTRA_HEADERS.
# never receive the main endpoint's headers or body fields; it has
# its own DEDUPE_LLM_EXTRA_HEADERS and DEDUPE_LLM_EXTRA_BODY.
await preflight_request(
deduper,
model_name=dedupe_model,
extra_headers=settings.dedupe.extra_headers,
extra_body=settings.dedupe.extra_body,
timeout=llm.preflight_timeout,
api_base_setting="DEDUPE_LLM_API_BASE",
)

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@ -116,6 +116,7 @@ async def preflight_model_connection(
model,
model_name=model_name,
extra_headers=resolved_settings.llm.extra_headers,
extra_body=resolved_settings.llm.extra_body,
timeout=resolved_settings.llm.preflight_timeout,
api_base_setting="LLM_API_BASE",
)
@ -126,6 +127,7 @@ async def preflight_request(
*,
model_name: str,
extra_headers: dict[str, str] | None,
extra_body: dict[str, Any] | None,
timeout: int,
api_base_setting: str,
) -> None:
@ -144,6 +146,7 @@ async def preflight_request(
request_timeout=timeout,
prompt_cache=False,
extra_headers=extra_headers,
extra_body=extra_body,
has_tools=False,
)
try:

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@ -307,6 +307,7 @@ async def _summarize(model: str, prompt: str, max_tokens: int) -> str | None:
request_timeout=llm.timeout,
prompt_cache=False,
extra_headers=llm.extra_headers,
extra_body=llm.extra_body,
has_tools=False,
).resolve(ModelSettings(max_tokens=max_tokens))
try:

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@ -39,11 +39,13 @@ def _dedupe_model_settings(
model_name=model_name,
force_required_tool_choice=False,
request_timeout=request_timeout,
# The main model's headers apply only when dedupe falls back to the main
# model; a dedicated dedupe model may route to another provider, which
# must never receive the main endpoint's credentials. A dedicated model
# gets its own DEDUPE_LLM_EXTRA_HEADERS instead.
# The main model's headers and body fields apply only when dedupe falls
# back to the main model; a dedicated dedupe model may route to another
# provider, which must never receive the main endpoint's credentials or
# identifiers. A dedicated model gets its own DEDUPE_LLM_EXTRA_HEADERS
# and DEDUPE_LLM_EXTRA_BODY instead.
extra_headers=dedupe.extra_headers if dedupe.model else llm.extra_headers,
extra_body=dedupe.extra_body if dedupe.model else llm.extra_body,
has_tools=False,
)

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@ -147,7 +147,9 @@ def _patch_budget(monkeypatch: pytest.MonkeyPatch, *, keep_tokens: int, window:
context.auto_compact = True
settings = SimpleNamespace(
context=context,
llm=SimpleNamespace(api_key=None, api_base=None, timeout=1, extra_headers=None),
llm=SimpleNamespace(
api_key=None, api_base=None, timeout=1, extra_headers=None, extra_body=None
),
)
monkeypatch.setattr(compaction, "load_settings", lambda: settings)
@ -228,7 +230,11 @@ async def test_summarize_routes_through_provider_with_settings(
"load_settings",
lambda: SimpleNamespace(
llm=SimpleNamespace(
api_key=None, api_base=None, timeout=1, extra_headers={"X-Feature-Key": "svc"}
api_key=None,
api_base=None,
timeout=1,
extra_headers={"X-Feature-Key": "svc"},
extra_body={"service_tier": "flex"},
)
),
)
@ -239,6 +245,7 @@ async def test_summarize_routes_through_provider_with_settings(
assert captured["model"] == "litellm/openai/some-model"
settings = captured["model_settings"]
assert settings.extra_headers == {"X-Feature-Key": "svc"}
assert settings.extra_body == {"service_tier": "flex"}
assert settings.max_tokens == 64

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@ -37,6 +37,12 @@ _LLM_ENV_KEYS = [
"STRIX_RUNTIME_BACKEND",
# TelemetrySettings
"STRIX_TELEMETRY",
# JSON-object settings
"LLM_EXTRA_HEADERS",
"LLM_EXTRA_BODY",
"STRIX_DEDUPE_MODEL",
"DEDUPE_LLM_EXTRA_HEADERS",
"DEDUPE_LLM_EXTRA_BODY",
]
@ -406,6 +412,57 @@ def test_persist_current_replaces_corrupt_file(
assert json.loads(target.read_text(encoding="utf-8")) == {"env": {"STRIX_LLM": "env-model"}}
@pytest.mark.parametrize(
("env_var", "section", "field"),
[
("LLM_EXTRA_HEADERS", "llm", "extra_headers"),
("LLM_EXTRA_BODY", "llm", "extra_body"),
("DEDUPE_LLM_EXTRA_HEADERS", "dedupe", "extra_headers"),
("DEDUPE_LLM_EXTRA_BODY", "dedupe", "extra_body"),
],
)
def test_persisted_json_object_setting_reloads_without_env(
tmp_path: Path, monkeypatch: pytest.MonkeyPatch, env_var: str, section: str, field: str
) -> None:
loader.apply_config_override(tmp_path / "cli-config.json")
monkeypatch.setenv(env_var, '{"X-Tenant": "acme"}')
loader.load_settings()
loader.persist_current()
monkeypatch.delenv(env_var)
loader.apply_config_override(tmp_path / "cli-config.json")
settings = loader.load_settings()
assert getattr(getattr(settings, section), field) == {"X-Tenant": "acme"}
def test_json_object_setting_saved_as_object_still_loads(tmp_path: Path) -> None:
target = tmp_path / "cli-config.json"
target.write_text(
json.dumps({"env": {"LLM_EXTRA_BODY": {"service_tier": "flex"}}}), encoding="utf-8"
)
loader.apply_config_override(target)
assert loader.load_settings().llm.extra_body == {"service_tier": "flex"}
def test_invalid_saved_json_object_setting_is_rejected(tmp_path: Path) -> None:
target = tmp_path / "cli-config.json"
target.write_text(json.dumps({"env": {"LLM_EXTRA_BODY": "not json"}}), encoding="utf-8")
loader.apply_config_override(target)
with pytest.raises(ValidationError):
loader.load_settings()
def test_saved_string_setting_is_not_json_decoded(tmp_path: Path) -> None:
target = tmp_path / "cli-config.json"
target.write_text(json.dumps({"env": {"STRIX_LLM": '{"not": "decoded"}'}}), encoding="utf-8")
loader.apply_config_override(target)
assert loader.load_settings().llm.model == '{"not": "decoded"}'
def test_persist_current_sets_0600_mode(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("STRIX_LLM", "persisted-model")
target = tmp_path / "cli-config.json"

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@ -148,3 +148,42 @@ def test_config_file_loads_dedupe_model(
assert settings.dedupe.reasoning_effort == "minimal"
# Main model stays independent of the dedupe override.
assert settings.llm.model == "openai/root"
def test_dedicated_dedupe_model_uses_own_body_fields_not_main(
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.setenv("LLM_EXTRA_BODY", json.dumps({"safety_identifier": "main"}))
loader._cached = None
try:
dedupe = DedupeSettings(
STRIX_DEDUPE_MODEL="deepseek/cheap",
DEDUPE_LLM_EXTRA_BODY={"service_tier": "flex"},
)
settings = _dedupe_model_settings(dedupe, "deepseek/cheap", 300)
assert settings.extra_body == {"service_tier": "flex"}
finally:
loader._cached = None
def test_dedicated_dedupe_model_gets_no_main_body_fields_by_default(
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.setenv("LLM_EXTRA_BODY", json.dumps({"safety_identifier": "main"}))
loader._cached = None
try:
dedupe = DedupeSettings(STRIX_DEDUPE_MODEL="deepseek/cheap")
settings = _dedupe_model_settings(dedupe, "deepseek/cheap", 300)
assert settings.extra_body is None
finally:
loader._cached = None
def test_fallback_dedupe_inherits_main_body_fields(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("LLM_EXTRA_BODY", json.dumps({"safety_identifier": "main"}))
loader._cached = None
try:
settings = _dedupe_model_settings(DedupeSettings(), "openai/main-model", 300)
assert settings.extra_body == {"safety_identifier": "main"}
finally:
loader._cached = None

View file

@ -361,6 +361,58 @@ def test_make_model_settings_extra_headers_survive_reasoning_resolve() -> None:
assert settings.extra_headers == {"X-Feature-Key": "svc"}
def test_make_model_settings_sets_extra_body() -> None:
settings = make_model_settings(
"none",
model_name="openai/gpt-5",
extra_body={"service_tier": "flex", "safety_identifier": "user-hash"},
)
assert settings.extra_body == {"service_tier": "flex", "safety_identifier": "user-hash"}
def test_make_model_settings_omits_extra_body_when_unset() -> None:
assert make_model_settings("none", model_name="gpt-4o").extra_body is None
def test_make_model_settings_extra_body_survives_reasoning_resolve() -> None:
settings = make_model_settings(
"high",
model_name="openai/o3",
extra_body={"service_tier": "flex"},
)
assert settings.extra_body == {"service_tier": "flex"}
def test_make_model_settings_extra_body_merges_with_max_reasoning_field() -> None:
settings = make_model_settings(
"max",
model_name="deepseek/deepseek-v4-flash",
extra_body={"safety_identifier": "user-hash"},
)
assert settings.extra_body == {"safety_identifier": "user-hash", "reasoning_effort": "max"}
def test_make_model_settings_max_reasoning_overrides_extra_body_reasoning_field() -> None:
settings = make_model_settings(
"max",
model_name="deepseek/deepseek-v4-flash",
extra_body={"reasoning_effort": "low"},
)
assert settings.extra_body == {"reasoning_effort": "max"}
def test_make_model_settings_does_not_mutate_caller_extra_body() -> None:
extra_body = {"safety_identifier": "user-hash"}
make_model_settings("max", model_name="deepseek/deepseek-v4-flash", extra_body=extra_body)
assert extra_body == {"safety_identifier": "user-hash"}
def test_make_model_settings_timeout_survives_reasoning_resolve() -> None:
# Reasoning is resolved via ModelSettings.resolve(); the timeout in extra_args
# must not be dropped when a reasoning override is merged in.

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@ -0,0 +1,97 @@
"""Tests for LLM_EXTRA_BODY: extra fields merged into every LLM request body."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any
import httpx
import litellm
import openai
import pytest
from agents.extensions.models.litellm_model import LitellmModel
from agents.models.interface import ModelTracing
from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel
from agents.models.openai_responses import OpenAIResponsesModel
from strix.config import loader
from strix.config.loader import load_settings
from strix.core.inputs import make_model_settings
if TYPE_CHECKING:
from collections.abc import Iterator
from agents.models.interface import Model
_EXTRA_BODY = {"service_tier": "flex", "safety_identifier": "user-hash"}
@pytest.fixture(autouse=True)
def _reset(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]:
monkeypatch.delenv("LLM_EXTRA_BODY", raising=False)
monkeypatch.setattr(loader, "_cached", None)
monkeypatch.setattr(loader, "_override", None)
yield
def test_extra_body_parsed_from_json_env(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("LLM_EXTRA_BODY", json.dumps({"service_tier": "flex", "n": 1}))
assert load_settings().llm.extra_body == {"service_tier": "flex", "n": 1}
async def _send(model: Model) -> None:
await model.get_response(
system_instructions=None,
input="Reply with just 'OK'.",
model_settings=make_model_settings(None, model_name="gpt-5", extra_body=_EXTRA_BODY),
tools=[],
output_schema=None,
handoffs=[],
tracing=ModelTracing.DISABLED,
previous_response_id=None,
conversation_id=None,
prompt=None,
)
def _capturing_client(captured: dict[str, Any]) -> openai.AsyncOpenAI:
def handler(request: httpx.Request) -> httpx.Response:
captured.update(json.loads(request.content))
return httpx.Response(400, json={"error": {"message": "captured"}})
return openai.AsyncOpenAI(
api_key="test",
base_url="https://gateway.example/v1",
max_retries=0,
http_client=httpx.AsyncClient(transport=httpx.MockTransport(handler)),
)
@pytest.mark.asyncio
@pytest.mark.parametrize("model_cls", [OpenAIChatCompletionsModel, OpenAIResponsesModel])
async def test_extra_body_reaches_native_openai_request_body(model_cls: type) -> None:
captured: dict[str, Any] = {}
with pytest.raises(openai.BadRequestError):
await _send(model_cls(model="gpt-5", openai_client=_capturing_client(captured)))
assert {k: captured.get(k) for k in _EXTRA_BODY} == _EXTRA_BODY
@pytest.mark.asyncio
async def test_extra_body_passed_to_litellm(monkeypatch: pytest.MonkeyPatch) -> None:
captured: dict[str, Any] = {}
async def fake_acompletion(**kwargs: Any) -> Any:
captured.update(kwargs)
raise RuntimeError("captured")
monkeypatch.setattr(litellm, "acompletion", fake_acompletion)
with pytest.raises(RuntimeError, match="captured"):
await _send(LitellmModel(model="openai/gpt-5", base_url="https://gateway.example/v1"))
assert captured["extra_body"] == _EXTRA_BODY

View file

@ -60,6 +60,7 @@ def test_preflight_request_fails_after_its_own_timeout_with_a_clear_message() ->
model, # type: ignore[arg-type]
model_name="openai/gpt-4o",
extra_headers=None,
extra_body=None,
timeout=1,
api_base_setting="DEDUPE_LLM_API_BASE",
)
@ -130,3 +131,52 @@ def test_warm_up_checks_the_dedupe_model_with_its_own_headers_and_the_preflight_
"DEDUPE_LLM_API_BASE",
]
assert calls[1][0] is dedupe_model
def test_warm_up_checks_each_model_with_its_own_body_fields(
monkeypatch: pytest.MonkeyPatch, tmp_path: Path
) -> None:
monkeypatch.setenv("STRIX_LLM", "openai/gpt-4o")
monkeypatch.setenv("LLM_API_KEY", "sk-test")
monkeypatch.setenv("LLM_EXTRA_BODY", '{"safety_identifier": "main"}')
monkeypatch.setenv("STRIX_DEDUPE_MODEL", "anthropic/claude-sonnet-4-5")
monkeypatch.setenv("DEDUPE_LLM_EXTRA_BODY", '{"service_tier": "flex"}')
_fresh_settings(monkeypatch, tmp_path)
calls: list[dict[str, Any]] = []
async def record(_model: Any, **kwargs: Any) -> None:
calls.append(kwargs)
monkeypatch.setattr(scan_setup, "preflight_request", record)
monkeypatch.setattr(cli_main, "preflight_request", record)
monkeypatch.setattr("strix.config.models.configure_sdk_model_defaults", lambda _settings: None)
monkeypatch.setattr("strix.report.dedupe.resolve_dedupe_model", lambda _dedupe, _name: object())
asyncio.run(cli_main.warm_up_llm())
assert [kwargs["extra_body"] for kwargs in calls] == [
{"safety_identifier": "main"},
{"service_tier": "flex"},
]
def test_preflight_request_sends_the_extra_body() -> None:
seen: list[Any] = []
class _Recorder:
async def get_response(self, *, model_settings: Any, **_: Any) -> None:
seen.append(model_settings.extra_body)
asyncio.run(
preflight_request(
_Recorder(), # type: ignore[arg-type]
model_name="openai/gpt-4o",
extra_headers=None,
extra_body={"safety_identifier": "main"},
timeout=5,
api_base_setting="LLM_API_BASE",
)
)
assert seen == [{"safety_identifier": "main"}]

View file

@ -28,6 +28,7 @@ def _wire_runner(monkeypatch: pytest.MonkeyPatch, tmp_path: Any) -> None:
timeout=300,
prompt_cache=True,
extra_headers=None,
extra_body=None,
),
runtime=types.SimpleNamespace(max_context_images=3),
)

View file

@ -44,6 +44,7 @@ def _settings() -> Any:
timeout=300,
prompt_cache=True,
extra_headers=None,
extra_body=None,
),
runtime=types.SimpleNamespace(max_context_images=3),
)

View file

@ -42,6 +42,7 @@ async def test_persistent_rate_limit_stops_gracefully(
timeout=300,
prompt_cache=True,
extra_headers=None,
extra_body=None,
),
runtime=types.SimpleNamespace(max_context_images=3),
)

View file

@ -64,6 +64,7 @@ def _patch_engine_scaffold(
timeout=300,
prompt_cache=True,
extra_headers=None,
extra_body=None,
),
runtime=types.SimpleNamespace(max_context_images=3),
)

View file

@ -52,6 +52,7 @@ def _settings() -> Any:
timeout=300,
prompt_cache=True,
extra_headers=None,
extra_body=None,
),
runtime=types.SimpleNamespace(max_context_images=3),
)