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"""OpenAI client setup with configuration support."""
import logging
import os
import uuid
from typing import Optional
import backoff
import litellm
from agents import (
set_default_openai_client,
set_tracing_disabled,
)
from openai import AsyncOpenAI
from ..config import get_config, get_config_manager
from .model_deprecation import check_model_deprecation
from .model_info import resolve_model_alias
# Suppress debug info from litellm
litellm.suppress_debug_info = True
litellm.drop_params = True
# Register OAuth providers with LiteLLM
# This must happen early so custom providers are available for model routing
_oauth_providers_registered = False
# LiteLLM changed exception namespace in some versions; unify access.
_LITELLM_EXC = getattr(litellm, "exceptions", litellm)
LITELLM_RETRYABLE_ERRORS = (
getattr(_LITELLM_EXC, "ServiceUnavailableError", Exception),
getattr(_LITELLM_EXC, "RateLimitError", Exception),
getattr(_LITELLM_EXC, "APIConnectionError", Exception),
getattr(_LITELLM_EXC, "Timeout", Exception),
getattr(_LITELLM_EXC, "InternalServerError", Exception),
)
# Configure global retry settings for LiteLLM
# num_retries will be applied to all litellm API calls unless overridden
litellm.num_retries = 3 # Default, will be updated with config values
litellm.num_retries_per_request = 3
# Well-known environment variable mappings for common providers
# For providers not listed here, the api_key from config will be set
# to the provider's expected env var (e.g., {PROVIDER}_API_KEY)
# OAuth provider to LiteLLM provider mapping
# OAuth providers use different names than LiteLLM expects
OAUTH_TO_LITELLM_PROVIDER = {
"google": "gemini", # google OAuth → gemini/ for LiteLLM
"claude": "anthropic", # claude OAuth → anthropic/ for LiteLLM
"chatgpt": "openai", # chatgpt OAuth → openai/ for LiteLLM
# antigravity uses custom API, not LiteLLM routing
}
PROVIDER_ENV_VARS = {
"openai": {"api_key": "OPENAI_API_KEY", "base_url": "OPENAI_BASE_URL"},
"anthropic": {"api_key": "ANTHROPIC_API_KEY", "base_url": "ANTHROPIC_BASE_URL"},
"google": {"api_key": "GOOGLE_API_KEY"},
"gemini": {"api_key": "GEMINI_API_KEY"},
"azure": {
"api_key": "AZURE_API_KEY",
"base_url": "AZURE_API_BASE",
"api_version": "AZURE_API_VERSION",
},
"vertex_ai": {
"credentials_path": "GOOGLE_APPLICATION_CREDENTIALS",
"location": "VERTEXAI_LOCATION",
},
"bedrock": {"api_key": "AWS_ACCESS_KEY_ID"},
"cohere": {"api_key": "COHERE_API_KEY"},
"replicate": {"api_key": "REPLICATE_API_TOKEN"},
"huggingface": {"api_key": "HUGGINGFACE_API_KEY"},
"together_ai": {"api_key": "TOGETHERAI_API_KEY"},
"openrouter": {"api_key": "OPENROUTER_API_KEY"},
"deepinfra": {"api_key": "DEEPINFRA_API_KEY"},
"groq": {"api_key": "GROQ_API_KEY"},
"mistral": {"api_key": "MISTRAL_API_KEY"},
"perplexity": {"api_key": "PERPLEXITYAI_API_KEY"},
"fireworks_ai": {"api_key": "FIREWORKS_AI_API_KEY"},
"cloudflare": {"api_key": "CLOUDFLARE_API_KEY"},
"github_copilot": {"api_key": "GITHUB_TOKEN"},
"ollama": {"base_url": "OLLAMA_BASE_URL"},
"custom": {"api_key": "OPENAI_API_KEY", "base_url": "OPENAI_BASE_URL"},
}
def _ensure_oauth_providers_registered() -> None:
"""Ensure OAuth providers are registered with LiteLLM.
This function is idempotent and will only register providers once.
"""
global _oauth_providers_registered
if _oauth_providers_registered:
return
try:
from ..auth.litellm_oauth import register_oauth_providers
register_oauth_providers()
_oauth_providers_registered = True
except ImportError:
pass # Auth module not available
def _get_provider_env_var_name(provider: str) -> str:
"""Get the expected API key environment variable name for a provider."""
provider_lower = provider.lower()
if provider_lower in PROVIDER_ENV_VARS:
return PROVIDER_ENV_VARS[provider_lower].get("api_key", f"{provider.upper()}_API_KEY")
# Default pattern for unknown providers
return f"{provider.upper()}_API_KEY"
def get_provider_api_env_var(provider: str) -> str:
"""Public helper so other modules can discover the provider's API key env var."""
return _get_provider_env_var_name(provider)
def _split_model_identifier(model: str) -> tuple[Optional[str], str, bool]:
"""Split a model identifier into provider/model parts.
Returns:
(provider, model_name, had_litellm_prefix)
"""
if not model:
return None, "", False
remainder = model
had_prefix = False
if remainder.startswith("litellm/"):
had_prefix = True
remainder = remainder[len("litellm/") :]
if "/" not in remainder:
return None, remainder, had_prefix
provider_part, model_part = remainder.split("/", 1)
return provider_part.lower(), model_part, had_prefix
def _is_openai_native_model(raw_model: str) -> bool:
"""
Detect whether a model string refers to an OpenAI-native model.
Handles plain names ("gpt-4o") and provider-prefixed strings
("openai/gpt-4o", "litellm/openai/gpt-4o").
"""
if not raw_model:
return False
_, model_part, _ = _split_model_identifier(raw_model)
ml = model_part.lower()
return ml.startswith(("gpt-", "o1-", "o3-", "o4-", "chatgpt-"))
def _strip_matching_provider(raw_model: str, provider: str) -> str:
"""Strip provider prefix if it matches the resolved provider."""
explicit_provider, model_part, _ = _split_model_identifier(raw_model)
if explicit_provider and explicit_provider == provider.lower():
return model_part
return raw_model
def _resolve_model_settings():
"""Resolve the effective config, provider, and raw model string.
When model comes from KODER_MODEL env var, it may use 'provider/model' format
(e.g., 'openrouter/x-ai/grok-4.1-fast:free'), so we extract the provider from it.
When model comes from config file, the provider is explicitly specified in
config.model.provider, so we use that directly without parsing the model name.
Model names can contain '/' (e.g., 'x-ai/grok-4.1-fast:free') which should not
be interpreted as a provider prefix.
"""
config = get_config()
config_manager = get_config_manager()
# Check if KODER_MODEL env var is set
env_model = os.environ.get("KODER_MODEL")
model_from_env = env_model is not None
raw_model = config_manager.get_effective_value(config.model.name, "KODER_MODEL")
# Resolve model aliases (e.g., 'sonnet' → 'claude-sonnet-4-6')
raw_model = resolve_model_alias(raw_model)
provider = config.model.provider.lower()
# Only extract provider from model string if it came from environment variable
# Environment variable uses 'provider/model' format (e.g., 'openrouter/deepseek-r1')
# Config file has separate 'provider' field, so model name may contain '/' as part of name
if model_from_env:
explicit_provider, _, _ = _split_model_identifier(raw_model)
if explicit_provider:
provider = explicit_provider
return config, config_manager, provider, raw_model, model_from_env
def _get_provider_api_key(config, config_manager, provider: str):
"""Get API key with priority: KODER_API_KEY > OAuth > ENV > Config."""
koder_api_key = os.environ.get("KODER_API_KEY")
if koder_api_key:
return koder_api_key
try:
from ..auth.client_integration import get_oauth_api_key, map_provider_to_oauth
oauth_provider = map_provider_to_oauth(provider)
if oauth_provider:
oauth_key = get_oauth_api_key(oauth_provider)
if oauth_key:
return oauth_key
except ImportError:
pass
env_var_name = _get_provider_env_var_name(provider)
config_value = config.model.api_key if config.model.provider.lower() == provider else None
return config_manager.get_effective_value(config_value, env_var_name)
def _setup_provider_env_vars(config, provider: str):
"""Set up environment variables for the provider (used by LiteLLM)."""
config_provider = config.model.provider.lower()
if config_provider != provider:
return
# Set provider-specific env vars from config if not already set
if config_provider == "azure":
if config.model.azure_api_version and not os.environ.get("AZURE_API_VERSION"):
os.environ["AZURE_API_VERSION"] = config.model.azure_api_version
if config.model.base_url and not os.environ.get("AZURE_API_BASE"):
os.environ["AZURE_API_BASE"] = config.model.base_url
elif config_provider == "vertex_ai":
if config.model.vertex_ai_credentials_path and not os.environ.get(
"GOOGLE_APPLICATION_CREDENTIALS"
):
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = config.model.vertex_ai_credentials_path
if config.model.vertex_ai_location and not os.environ.get("VERTEXAI_LOCATION"):
os.environ["VERTEXAI_LOCATION"] = config.model.vertex_ai_location
# Set API key if configured and not in env
api_key = config.model.api_key
if api_key:
env_var_name = _get_provider_env_var_name(config_provider)
if not os.environ.get(env_var_name):
os.environ[env_var_name] = api_key
def _map_oauth_to_litellm_provider(provider: str) -> str:
"""Map OAuth provider names to LiteLLM-compatible provider names.
OAuth providers use different names than LiteLLM expects:
- google (OAuth) → gemini (LiteLLM)
- claude (OAuth) → anthropic (LiteLLM)
- chatgpt (OAuth) → openai (LiteLLM)
Args:
provider: Provider name (e.g., 'google', 'claude', 'chatgpt')
Returns:
LiteLLM-compatible provider name
"""
return OAUTH_TO_LITELLM_PROVIDER.get(provider.lower(), provider.lower())
def _should_use_oauth_provider(provider: str) -> bool:
"""Check if we should use OAuth custom handler for a provider.
Returns True if:
1. The provider is an OAuth provider (google, claude, chatgpt, antigravity)
2. A valid OAuth token exists for the provider
Args:
provider: Provider name to check
Returns:
True if OAuth custom handler should be used
"""
try:
from ..auth.client_integration import has_oauth_credentials, map_provider_to_oauth
from ..auth.litellm_oauth import is_oauth_provider
if not is_oauth_provider(provider):
return False
oauth_provider = map_provider_to_oauth(provider)
if oauth_provider and has_oauth_credentials(oauth_provider):
return True
except ImportError:
pass
return False
def _get_oauth_model_prefix(provider: str) -> Optional[str]:
"""Get the LiteLLM custom provider prefix for OAuth access.
Args:
provider: OAuth provider name (google, claude, chatgpt, antigravity)
Returns:
Custom provider prefix (e.g., 'google_oauth') or None
"""
try:
from ..auth.litellm_oauth import get_oauth_model_prefix
return get_oauth_model_prefix(provider)
except ImportError:
return None
def _normalize_model_name(provider: str, raw_model: str, model_from_env: bool = False) -> str:
"""Return a LiteLLM-compatible identifier, always using litellm/<provider>/<model>.
When OAuth tokens are available for the provider, uses custom OAuth handler
(e.g., 'google_oauth/gemini-pro') instead of standard LiteLLM provider.
Args:
provider: The resolved provider name
raw_model: The raw model string
model_from_env: If True, the model string came from KODER_MODEL env var and may
contain an explicit provider prefix (e.g., 'openrouter/model-name').
If False, the model name should be used as-is since provider comes
from config file.
"""
if not raw_model:
return raw_model
if raw_model.startswith("litellm/"):
return raw_model
# Ensure OAuth providers are registered before checking
_ensure_oauth_providers_registered()
# Only parse provider from model string if it came from environment variable
if model_from_env:
explicit_provider, remainder, _ = _split_model_identifier(raw_model)
if explicit_provider:
# Check if OAuth should be used for this provider
if _should_use_oauth_provider(explicit_provider):
oauth_prefix = _get_oauth_model_prefix(explicit_provider)
if oauth_prefix:
return f"litellm/{oauth_prefix}/{remainder}"
# Fall back to standard LiteLLM provider mapping
litellm_provider = _map_oauth_to_litellm_provider(explicit_provider)
return f"litellm/{litellm_provider}/{remainder}"
# Check if OAuth should be used for this provider
if _should_use_oauth_provider(provider):
oauth_prefix = _get_oauth_model_prefix(provider)
if oauth_prefix:
return f"litellm/{oauth_prefix}/{raw_model}"
# Fall back to standard LiteLLM provider mapping
litellm_provider = _map_oauth_to_litellm_provider(provider)
return f"litellm/{litellm_provider}/{raw_model}"
def _compute_effective_model(config, config_manager, provider, raw_model, model_from_env=False):
"""Determine the model name and whether to use native OpenAI integration."""
api_key = _get_provider_api_key(config, config_manager, provider)
use_native = provider in ("openai", "custom") and api_key and _is_openai_native_model(raw_model)
if use_native:
# If the raw model string included a provider prefix, strip it for native calls
normalized_raw = _strip_matching_provider(raw_model, provider)
return normalized_raw, True, api_key
return _normalize_model_name(provider, raw_model, model_from_env), False, api_key
def _resolve_completion_settings(model_override: Optional[str] = None):
"""Resolve provider/model settings for a completion call, honoring overrides."""
config, config_manager, provider, raw_model, model_from_env = _resolve_model_settings()
if model_override is not None:
# Resolve aliases in overrides (e.g., 'sonnet' → 'claude-sonnet-4-6')
raw_model = resolve_model_alias(model_override)
override_provider, _, _ = _split_model_identifier(raw_model)
if override_provider is not None:
provider = override_provider
model_from_env = True
else:
model_from_env = False
return config, config_manager, provider, raw_model, model_from_env
def get_model_name(model_override: Optional[str] = None):
"""Get the appropriate model name with priority: ENV > Config > Default."""
config, config_manager, provider, raw_model, model_from_env = _resolve_completion_settings(
model_override
)
model, _, _ = _compute_effective_model(
config, config_manager, provider, raw_model, model_from_env
)
# Check for model deprecation and warn if applicable
try:
warning = check_model_deprecation(raw_model)
if warning:
logging.warning(warning)
except Exception:
pass # Don't fail if deprecation check fails
return model
def resolve_model_override_name(raw_model: str) -> str:
"""Resolve a model override into the actual call model name."""
config, config_manager, provider, raw_model, model_from_env = _resolve_completion_settings(
raw_model
)
model, _, _ = _compute_effective_model(
config, config_manager, provider, raw_model, model_from_env
)
return model
def get_api_key(model_override: Optional[str] = None):
"""Get API key with priority: KODER_API_KEY > OAuth > ENV > Config."""
config, config_manager, provider, _, _ = _resolve_completion_settings(model_override)
return _get_provider_api_key(config, config_manager, provider)
def _resolve_base_url(config, config_manager, provider: str) -> Optional[str]:
"""Resolve base URL with priority: KODER_BASE_URL > ENV > Config.
Args:
config: Configuration object
config_manager: Configuration manager
provider: Provider name
Returns:
Base URL or None
"""
koder_base_url = os.environ.get("KODER_BASE_URL")
if koder_base_url:
return koder_base_url
base_url_env_var = PROVIDER_ENV_VARS.get(provider, {}).get(
"base_url", f"{provider.upper()}_BASE_URL"
)
base_url_config = config.model.base_url if config.model.provider.lower() == provider else None
return config_manager.get_effective_value(base_url_config, base_url_env_var)
def get_base_url(model_override: Optional[str] = None):
"""Get base URL with priority: KODER_BASE_URL > ENV > Config."""
config, config_manager, provider, _, _ = _resolve_completion_settings(model_override)
return _resolve_base_url(config, config_manager, provider)
def _get_oauth_extra_headers(provider: str) -> Optional[dict]:
"""Get OAuth-specific headers for a provider.
Args:
provider: Provider identifier
Returns:
Dict of extra headers or None
"""
try:
from ..auth.client_integration import (
get_oauth_headers,
has_oauth_token,
map_provider_to_oauth,
)
oauth_provider = map_provider_to_oauth(provider)
if oauth_provider and has_oauth_token(oauth_provider):
headers = get_oauth_headers(oauth_provider)
# Remove Authorization header as it's handled via api_key
headers.pop("Authorization", None)
return headers if headers else None
except ImportError:
pass
return None
def get_litellm_model_kwargs(model_override: Optional[str] = None) -> dict:
"""Get kwargs for creating a LitellmModel instance.
Returns a dict with 'model', 'api_key', 'base_url', and retry configuration
that can be passed directly to LitellmModel constructor.
"""
config, config_manager, provider, raw_model, model_from_env = _resolve_completion_settings(
model_override
)
# Get normalized model name for LiteLLM
model = _normalize_model_name(provider, raw_model, model_from_env)
# Strip the 'litellm/' prefix since LitellmModel adds it internally
if model.startswith("litellm/"):
model = model[len("litellm/") :]
api_key = _get_provider_api_key(config, config_manager, provider)
base_url = _resolve_base_url(config, config_manager, provider)
kwargs = {
"model": model,
"api_key": api_key,
"base_url": base_url,
"max_retries": 3,
}
# Add OAuth-specific headers if available
oauth_headers = _get_oauth_extra_headers(provider)
if oauth_headers:
kwargs["extra_headers"] = oauth_headers
return kwargs
def get_model_client_snapshot(model_override: Optional[str] = None) -> dict:
"""Return a stable snapshot of the currently resolved client settings."""
config = get_config()
config_manager = get_config_manager()
return {
"model_name": get_model_name(model_override),
"api_key": get_api_key(model_override),
"base_url": get_base_url(model_override),
"litellm_kwargs": get_litellm_model_kwargs(model_override),
"native_openai": is_native_openai_provider(model_override),
"reasoning_effort": config_manager.get_effective_value(
config.model.reasoning_effort,
"KODER_REASONING_EFFORT",
),
}
def is_native_openai_provider(model_override: Optional[str] = None) -> bool:
"""Check if the current provider should use native OpenAI client."""
config, config_manager, provider, raw_model, _ = _resolve_completion_settings(model_override)
api_key = _get_provider_api_key(config, config_manager, provider)
return (
provider in ("openai", "custom")
and api_key is not None
and _is_openai_native_model(raw_model)
)
@backoff.on_exception(
backoff.expo,
LITELLM_RETRYABLE_ERRORS,
max_tries=3,
jitter=backoff.full_jitter,
)
async def llm_completion(messages: list, model: Optional[str] = None) -> str:
"""
Make an LLM completion call using the configured provider settings.
This function reuses the same configuration as the main agent, ensuring
consistent API key and model settings. Includes automatic retry for 429 errors.
Args:
messages: List of message dicts with 'role' and 'content' keys
model: Optional model override. If None, uses configured model.
Returns:
The completion response content as string
"""
config, config_manager, provider, raw_model, model_from_env = _resolve_completion_settings(
model
)
# Ensure provider env vars are set (for litellm to pick up)
_setup_provider_env_vars(config, provider)
# Get model name and API key
model, use_native, api_key = _compute_effective_model(
config, config_manager, provider, raw_model, model_from_env
)
# When the model is OpenAI-native but llm_completion goes through litellm,
# ensure the model has a provider prefix so litellm can route it correctly.
# Without this, newer OpenAI models (e.g. gpt-5.4) that litellm doesn't
# recognize by name will fail with "LLM Provider NOT provided".
if use_native and not model.startswith(("openai/", "azure/")):
model = f"openai/{model}"
# Strip the 'litellm/' prefix since litellm.acompletion doesn't expect it
# (_compute_effective_model returns litellm-prefixed names for non-native models)
if model.startswith("litellm/"):
model = model[len("litellm/") :]
# Use the same base URL priority as the main agent path:
# KODER_BASE_URL > provider-specific environment > config.
base_url = _resolve_base_url(config, config_manager, provider)
# Build kwargs for litellm with retry configuration
kwargs = {
"model": model,
"messages": messages,
"metadata": {"source": "koder"},
}
if api_key:
kwargs["api_key"] = api_key
if base_url:
kwargs["base_url"] = base_url
model_lower = str(model).lower()
is_copilot = "github_copilot/" in model_lower
extra_headers = None
if is_copilot:
extra_headers = {
"copilot-integration-id": "vscode-chat",
"editor-version": "vscode/1.98.1",
"editor-plugin-version": "copilot-chat/0.26.7",
"user-agent": "GitHubCopilotChat/0.26.7",
"openai-intent": "conversation-panel",
"x-github-api-version": "2025-04-01",
"x-request-id": str(uuid.uuid4()),
"x-vscode-user-agent-library-version": "electron-fetch",
}
if is_copilot and "codex" in model_lower:
if not hasattr(litellm, "aresponses"):
raise RuntimeError(
"GitHub Copilot Codex models require LiteLLM Responses API support. "
"Please upgrade litellm to a version that provides `aresponses`."
)
responses_kwargs = {
"model": model,
"input": messages,
"metadata": {"source": "koder"},
"stream": False,
}
if api_key:
responses_kwargs["api_key"] = api_key
if base_url:
responses_kwargs["base_url"] = base_url
if extra_headers:
responses_kwargs["extra_headers"] = extra_headers
response = await litellm.aresponses(**responses_kwargs)
return _extract_responses_text(response)
if extra_headers:
kwargs["extra_headers"] = extra_headers
response = await litellm.acompletion(**kwargs)
return response.choices[0].message.content
def _extract_responses_text(response: object) -> str:
"""
Best-effort extraction of assistant text from a LiteLLM Responses API response.
Handles dict-like and pydantic-like objects.
"""
output_text = getattr(response, "output_text", None)
if isinstance(output_text, str) and output_text.strip():
return output_text
output = getattr(response, "output", None)
if output is None and isinstance(response, dict):
output = response.get("output")
if isinstance(output, list):
parts: list[str] = []
for item in output:
item_dict = item
if hasattr(item, "model_dump"):
try:
item_dict = item.model_dump()
except Exception:
item_dict = item
if not isinstance(item_dict, dict):
continue
if item_dict.get("type") == "message":
for content in item_dict.get("content", []) or []:
if isinstance(content, dict) and content.get("type") in (
"output_text",
"text",
):
text_val = content.get("text") or content.get("content")
if text_val:
parts.append(str(text_val))
elif item_dict.get("type") in ("output_text", "text"):
text_val = item_dict.get("text")
if text_val:
parts.append(str(text_val))
if parts:
return "".join(parts).strip()
if isinstance(response, dict):
try:
first = response.get("output", [])[0]
if isinstance(first, dict):
content = first.get("content", [])
if content and isinstance(content[0], dict):
return str(content[0].get("text", "")).strip()
except Exception:
pass
return ""
def setup_openai_client():
"""Set up the OpenAI client with priority: ENV > Config > Default.
Also configures global LiteLLM retry settings for all providers.
Registers OAuth custom providers with LiteLLM if auth module is available.
"""
set_tracing_disabled(True)
# Register OAuth providers with LiteLLM
_ensure_oauth_providers_registered()
config, config_manager, provider, raw_model, model_from_env = _resolve_model_settings()
# Setup provider environment variables for LiteLLM
_setup_provider_env_vars(config, provider)
model, use_native, api_key = _compute_effective_model(
config, config_manager, provider, raw_model, model_from_env
)
base_url = _resolve_base_url(config, config_manager, provider)
if use_native:
client = AsyncOpenAI(
api_key=api_key,
base_url=base_url,
max_retries=3,
)
set_default_openai_client(client)
return client
# Fall back to LiteLLM integration for other providers
return None