Torbjørn Lindahl
5add230d4c
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Implements a new provider that uses Google's Antigravity unified gateway API to access Claude, Gemini, and other models through a single OAuth2-authenticated endpoint. Features: - OAuth2 token management with automatic refresh - Multi-account rotation for rate limit distribution - Support for Claude Opus/Sonnet 4.5 (with/without thinking) - Support for Gemini 2.5/3 models (Pro/Flash variants) - Thinking mode support with configurable tokens - Image processing support - Dual quota pool tracking (antigravity vs gemini-cli) - Gemini-style API request format Authentication: - Reads from ANTIGRAVITY_REFRESH_TOKEN env var (priority) - Falls back to ~/.config/opencode/antigravity-accounts.json - Automatic token refresh with retry logic - Rate limit tracking per account and quota pool Files added: - providers/antigravity.py - Main provider implementation - providers/antigravity_auth.py - OAuth token manager - providers/registries/antigravity.py - Model registry - conf/antigravity_models.json - Model definitions (11 models) - docs/antigravity_provider.md - Setup and usage docs - tests/test_antigravity_provider.py - Unit tests (14 pass) Integration: - Added to provider priority order after ZEN - Registered in server.py with auto-detection - ToS warning logged on first use
577 lines
21 KiB
Python
577 lines
21 KiB
Python
"""Antigravity model provider implementation.
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Antigravity is Google's unified gateway API for accessing multiple AI models
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(Claude, Gemini, GPT-OSS) through a single Gemini-style interface.
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WARNING: Using this provider may violate Google's Terms of Service.
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See docs/antigravity_provider.md for important information about risks.
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"""
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from __future__ import annotations
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import base64
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import json
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import logging
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import re
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import uuid
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from typing import TYPE_CHECKING, Any, ClassVar
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import httpx
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from utils.env import get_env
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from utils.image_utils import validate_image
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from .antigravity_auth import AntigravityTokenManager
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from .base import ModelProvider
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from .registries.antigravity import AntigravityModelRegistry
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from .shared import ModelCapabilities, ModelResponse, ProviderType
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if TYPE_CHECKING:
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from tools.models import ToolModelCategory
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logger = logging.getLogger(__name__)
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# Antigravity API endpoints
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PRODUCTION_ENDPOINT = "https://cloudcode-pa.googleapis.com"
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DAILY_ENDPOINT = "https://daily-cloudcode-pa.sandbox.googleapis.com"
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# API path for content generation
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GENERATE_CONTENT_PATH = "/v1internal:generateContent"
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STREAM_GENERATE_CONTENT_PATH = "/v1internal:streamGenerateContent"
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# ToS warning message
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TOS_WARNING = """
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================================================================================
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ANTIGRAVITY PROVIDER WARNING
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Using the Antigravity provider may violate Google's Terms of Service.
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Users have reported account bans when using this approach.
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HIGH-RISK SCENARIOS:
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- Fresh Google accounts have a very high chance of getting banned
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- New accounts with Pro/Ultra subscriptions are frequently flagged
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By using this provider, you acknowledge and accept all associated risks.
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See docs/antigravity_provider.md for more information.
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================================================================================
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"""
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class AntigravityProvider(ModelProvider):
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"""Provider for Google's Antigravity unified gateway API.
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Antigravity provides access to Claude, Gemini, and other models through a
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single Gemini-style API interface. This provider handles:
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* OAuth2 authentication with refresh token management
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* Multi-account rotation on rate limits
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* Request transformation to Gemini-style format
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* Extended thinking support for compatible models
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* Image processing for vision requests
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Configuration:
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ANTIGRAVITY_REFRESH_TOKEN: OAuth2 refresh token (env var override)
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ANTIGRAVITY_PROJECT_ID: Google Cloud project ID
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ANTIGRAVITY_BASE_URL: Custom endpoint URL (defaults to production)
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"""
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# Model registry for capability metadata
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_registry: ClassVar[AntigravityModelRegistry | None] = None
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MODEL_CAPABILITIES: ClassVar[dict[str, ModelCapabilities]] = {}
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# Thinking mode budgets (percentage of max_thinking_tokens)
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THINKING_BUDGETS = {
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"minimal": 0.005,
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"low": 0.08,
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"medium": 0.33,
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"high": 0.67,
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"max": 1.0,
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}
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# Track whether warning has been shown this session
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_warning_shown: ClassVar[bool] = False
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def __init__(self, api_key: str = "", **kwargs) -> None:
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"""Initialize Antigravity provider.
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Args:
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api_key: Not used (authentication via OAuth2 refresh tokens)
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**kwargs: Additional configuration options
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"""
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# Initialize registry
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if AntigravityProvider._registry is None:
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AntigravityProvider._registry = AntigravityModelRegistry()
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AntigravityProvider.MODEL_CAPABILITIES = dict(self._registry.model_map)
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logger.info("Antigravity provider loaded %d models", len(self._registry.model_map))
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super().__init__(api_key, **kwargs)
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# Initialize token manager
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self._token_manager = AntigravityTokenManager()
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# HTTP client for API requests
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self._http_client: httpx.Client | None = None
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# Base URL configuration
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self._base_url = get_env("ANTIGRAVITY_BASE_URL") or PRODUCTION_ENDPOINT
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# Show warning on first use
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if not AntigravityProvider._warning_shown:
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logger.warning(TOS_WARNING)
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AntigravityProvider._warning_shown = True
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self._invalidate_capability_cache()
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@property
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def http_client(self) -> httpx.Client:
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"""Lazy initialization of HTTP client."""
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if self._http_client is None:
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timeout = httpx.Timeout(
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connect=30.0,
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read=600.0, # 10 minutes for long responses
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write=60.0,
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pool=600.0,
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)
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self._http_client = httpx.Client(timeout=timeout, follow_redirects=True)
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return self._http_client
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# ------------------------------------------------------------------
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# Provider identity
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# ------------------------------------------------------------------
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def get_provider_type(self) -> ProviderType:
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"""Return the provider type."""
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return ProviderType.ANTIGRAVITY
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# ------------------------------------------------------------------
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# Capability surface
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# ------------------------------------------------------------------
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def _lookup_capabilities(
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self,
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canonical_name: str,
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requested_name: str | None = None,
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) -> ModelCapabilities | None:
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"""Fetch capabilities from the registry."""
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if self._registry:
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return self._registry.get_capabilities(canonical_name)
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return None
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def get_all_model_capabilities(self) -> dict[str, ModelCapabilities]:
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"""Return registry-backed capabilities."""
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if not self._registry:
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return {}
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return dict(self._registry.model_map)
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def list_models(
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self,
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*,
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respect_restrictions: bool = True,
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include_aliases: bool = True,
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lowercase: bool = False,
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unique: bool = False,
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) -> list[str]:
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"""Return available Antigravity model names."""
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if not self._registry:
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return []
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from utils.model_restrictions import get_restriction_service
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restriction_service = get_restriction_service() if respect_restrictions else None
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allowed_configs: dict[str, ModelCapabilities] = {}
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for model_name in self._registry.list_models():
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config = self._registry.resolve(model_name)
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if not config:
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continue
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if restriction_service:
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if not restriction_service.is_allowed(self.get_provider_type(), model_name):
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continue
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allowed_configs[model_name] = config
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if not allowed_configs:
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return []
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return ModelCapabilities.collect_model_names(
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allowed_configs,
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include_aliases=include_aliases,
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lowercase=lowercase,
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unique=unique,
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)
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def _resolve_model_name(self, model_name: str) -> str:
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"""Resolve aliases to canonical model names."""
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if self._registry:
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config = self._registry.resolve(model_name)
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if config and config.model_name != model_name:
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logger.debug("Resolved Antigravity alias '%s' to '%s'", model_name, config.model_name)
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return config.model_name
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return model_name
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# ------------------------------------------------------------------
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# Content generation
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# ------------------------------------------------------------------
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def generate_content(
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self,
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prompt: str,
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model_name: str,
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system_prompt: str | None = None,
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temperature: float = 0.7,
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max_output_tokens: int | None = None,
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thinking_mode: str = "medium",
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images: list[str] | None = None,
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**kwargs,
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) -> ModelResponse:
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"""Generate content using the Antigravity API.
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Args:
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prompt: User prompt to send to the model
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model_name: Model identifier (e.g., 'claude-opus-4-5-thinking')
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system_prompt: Optional system instructions
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temperature: Sampling temperature (0.0-2.0)
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max_output_tokens: Maximum tokens in response
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thinking_mode: Thinking budget level for thinking models
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images: Optional list of image paths or data URLs
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**kwargs: Additional parameters
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Returns:
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ModelResponse with generated content and metadata
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"""
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# Validate and resolve model name
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self.validate_parameters(model_name, temperature)
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capabilities = self.get_capabilities(model_name)
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resolved_model = self._resolve_model_name(model_name)
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# Get authentication
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access_token, project_id, fingerprint_headers = self._token_manager.get_access_token(model=resolved_model)
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# Build request
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request_body = self._build_request(
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prompt=prompt,
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model_name=resolved_model,
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project_id=project_id,
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system_prompt=system_prompt,
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temperature=temperature,
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max_output_tokens=max_output_tokens,
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thinking_mode=thinking_mode,
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images=images,
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capabilities=capabilities,
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)
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# Build headers
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headers = {
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"Authorization": f"Bearer {access_token}",
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"Content-Type": "application/json",
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**fingerprint_headers,
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}
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# Retry logic
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max_retries = 4
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retry_delays = [1.0, 3.0, 5.0, 8.0]
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attempt_counter = {"value": 0}
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def _attempt() -> ModelResponse:
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attempt_counter["value"] += 1
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url = f"{self._base_url}{GENERATE_CONTENT_PATH}"
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logger.debug("Antigravity request to %s for model %s", url, resolved_model)
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response = self.http_client.post(
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url,
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json=request_body,
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headers=headers,
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)
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# Handle errors
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if response.status_code != 200:
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self._handle_error_response(response, resolved_model)
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return self._parse_response(response.json(), resolved_model, capabilities, thinking_mode)
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try:
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return self._run_with_retries(
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operation=_attempt,
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max_attempts=max_retries,
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delays=retry_delays,
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log_prefix=f"Antigravity API ({resolved_model})",
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)
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except Exception as exc:
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attempts = max(attempt_counter["value"], 1)
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error_msg = f"Antigravity API error for {resolved_model} after {attempts} attempt(s): {exc}"
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raise RuntimeError(error_msg) from exc
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def _build_request(
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self,
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prompt: str,
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model_name: str,
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project_id: str,
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system_prompt: str | None = None,
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temperature: float = 0.7,
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max_output_tokens: int | None = None,
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thinking_mode: str = "medium",
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images: list[str] | None = None,
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capabilities: ModelCapabilities | None = None,
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) -> dict[str, Any]:
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"""Build the Antigravity API request body."""
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# Build contents array (Gemini-style format)
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contents = []
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# Build user message parts
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user_parts = []
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# Add text prompt
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user_parts.append({"text": prompt})
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# Add images if provided
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if images and capabilities and capabilities.supports_images:
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for image_path in images:
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try:
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image_part = self._process_image(image_path)
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if image_part:
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user_parts.append(image_part)
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except Exception as e:
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logger.warning("Failed to process image %s: %s", image_path, e)
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elif images:
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logger.warning("Model %s does not support images, ignoring %d image(s)", model_name, len(images))
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contents.append({"role": "user", "parts": user_parts})
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# Build generation config
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generation_config: dict[str, Any] = {
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"temperature": temperature,
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}
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if max_output_tokens:
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generation_config["maxOutputTokens"] = max_output_tokens
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# Add thinking config for thinking-capable models
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if capabilities and capabilities.supports_extended_thinking and thinking_mode in self.THINKING_BUDGETS:
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max_thinking = capabilities.max_thinking_tokens or 8192
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thinking_budget = int(max_thinking * self.THINKING_BUDGETS[thinking_mode])
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generation_config["thinkingConfig"] = {
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"thinkingBudget": thinking_budget,
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"includeThoughts": True,
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}
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# Ensure maxOutputTokens > thinkingBudget
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if not max_output_tokens or max_output_tokens <= thinking_budget:
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generation_config["maxOutputTokens"] = thinking_budget + 10000
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# Build system instruction (must be object with parts, not string)
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system_instruction = None
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if system_prompt:
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system_instruction = {"parts": [{"text": system_prompt}]}
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# Build full request
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request: dict[str, Any] = {
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"project": project_id,
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"model": model_name,
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"request": {
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"contents": contents,
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"generationConfig": generation_config,
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},
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"userAgent": "antigravity",
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"requestId": f"pal-{uuid.uuid4().hex[:12]}",
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}
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if system_instruction:
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request["request"]["systemInstruction"] = system_instruction
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return request
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def _parse_response(
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self,
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data: dict[str, Any],
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model_name: str,
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capabilities: ModelCapabilities | None,
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thinking_mode: str,
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) -> ModelResponse:
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"""Parse the Antigravity API response."""
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response_data = data.get("response", data)
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# Extract content from candidates
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content = ""
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thinking_content = ""
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candidates = response_data.get("candidates", [])
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if candidates:
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candidate = candidates[0]
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candidate_content = candidate.get("content", {})
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parts = candidate_content.get("parts", [])
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for part in parts:
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if part.get("thought"):
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# This is thinking content
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thinking_content += part.get("text", "")
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elif "thoughtSignature" in part and "text" in part:
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# Gemini thinking with signature
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thinking_content += part.get("text", "")
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elif "text" in part:
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content += part.get("text", "")
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# Extract usage metadata
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usage_metadata = response_data.get("usageMetadata", {})
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usage = {
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"input_tokens": usage_metadata.get("promptTokenCount", 0),
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"output_tokens": usage_metadata.get("candidatesTokenCount", 0),
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"total_tokens": usage_metadata.get("totalTokenCount", 0),
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}
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# Add thinking tokens if available
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if "thoughtsTokenCount" in usage_metadata:
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usage["thinking_tokens"] = usage_metadata["thoughtsTokenCount"]
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# Extract finish reason
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finish_reason = "UNKNOWN"
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if candidates:
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finish_reason = candidates[0].get("finishReason", "STOP")
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return ModelResponse(
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content=content,
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usage=usage,
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model_name=model_name,
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friendly_name="Antigravity",
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provider=ProviderType.ANTIGRAVITY,
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metadata={
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"thinking_mode": thinking_mode if capabilities and capabilities.supports_extended_thinking else None,
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"thinking_content": thinking_content if thinking_content else None,
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"finish_reason": finish_reason,
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"model_version": response_data.get("modelVersion"),
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"response_id": response_data.get("responseId"),
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"trace_id": data.get("traceId"),
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},
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)
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def _handle_error_response(self, response: httpx.Response, model_name: str) -> None:
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"""Handle error responses from the API."""
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try:
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error_data = response.json()
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error = error_data.get("error", {})
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error_code = error.get("code", response.status_code)
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error_message = error.get("message", response.text)
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error_status = error.get("status", "UNKNOWN")
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# Handle rate limiting
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if error_code == 429:
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# Extract retry delay from response
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retry_delay = 60.0
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details = error.get("details", [])
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for detail in details:
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if detail.get("@type", "").endswith("RetryInfo"):
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delay_str = detail.get("retryDelay", "60s")
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# Parse delay string (e.g., "3.957525076s")
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match = re.match(r"([\d.]+)s?", delay_str)
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if match:
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retry_delay = float(match.group(1))
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break
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# Mark current account as rate limited
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self._token_manager.mark_rate_limited(model_name, retry_delay)
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raise RuntimeError(f"Rate limited (429): {error_message}. Retry after {retry_delay}s")
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# Re-raise with formatted message
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raise RuntimeError(f"Antigravity API error {error_code} ({error_status}): {error_message}")
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except json.JSONDecodeError:
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raise RuntimeError(f"Antigravity API error {response.status_code}: {response.text}")
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def _process_image(self, image_path: str) -> dict[str, Any] | None:
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"""Process an image for the Antigravity API."""
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try:
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image_bytes, mime_type = validate_image(image_path)
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if image_path.startswith("data:"):
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# Extract base64 data from data URL
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_, data = image_path.split(",", 1)
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return {"inlineData": {"mimeType": mime_type, "data": data}}
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else:
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# Encode file bytes
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image_data = base64.b64encode(image_bytes).decode()
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return {"inlineData": {"mimeType": mime_type, "data": image_data}}
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except ValueError as e:
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logger.warning("Image validation failed: %s", e)
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return None
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except Exception as e:
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logger.error("Error processing image %s: %s", image_path, e)
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return None
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def _is_error_retryable(self, error: Exception) -> bool:
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"""Determine if an error should be retried."""
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error_str = str(error).lower()
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# Rate limits should be retried (we'll rotate accounts)
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if "429" in error_str or "rate limit" in error_str:
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return True
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# Check for retryable indicators
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retryable_indicators = [
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|
"timeout",
|
|
"connection",
|
|
"network",
|
|
"temporary",
|
|
"unavailable",
|
|
"retry",
|
|
"500",
|
|
"502",
|
|
"503",
|
|
"504",
|
|
]
|
|
|
|
return any(indicator in error_str for indicator in retryable_indicators)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Preferred model selection
|
|
# ------------------------------------------------------------------
|
|
|
|
def get_preferred_model(self, category: ToolModelCategory, allowed_models: list[str]) -> str | None:
|
|
"""Get preferred model for a category from allowed models."""
|
|
from tools.models import ToolModelCategory
|
|
|
|
if not allowed_models:
|
|
return None
|
|
|
|
capability_map = self.get_all_model_capabilities()
|
|
|
|
def find_best(candidates: list[str]) -> str | None:
|
|
return sorted(candidates, reverse=True)[0] if candidates else None
|
|
|
|
if category == ToolModelCategory.EXTENDED_REASONING:
|
|
# Prefer thinking-capable models
|
|
thinking_models = [
|
|
m for m in allowed_models if m in capability_map and capability_map[m].supports_extended_thinking
|
|
]
|
|
if thinking_models:
|
|
# Prefer Claude Opus, then Claude Sonnet, then Gemini
|
|
opus = [m for m in thinking_models if "opus" in m]
|
|
if opus:
|
|
return find_best(opus)
|
|
sonnet = [m for m in thinking_models if "sonnet" in m]
|
|
if sonnet:
|
|
return find_best(sonnet)
|
|
return find_best(thinking_models)
|
|
|
|
elif category == ToolModelCategory.FAST_RESPONSE:
|
|
# Prefer Flash models
|
|
flash_models = [m for m in allowed_models if "flash" in m]
|
|
if flash_models:
|
|
return find_best(flash_models)
|
|
|
|
# Default: prefer models by capability rank
|
|
return find_best(allowed_models)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Resource cleanup
|
|
# ------------------------------------------------------------------
|
|
|
|
def close(self) -> None:
|
|
"""Clean up resources."""
|
|
if self._http_client:
|
|
self._http_client.close()
|
|
self._http_client = None
|
|
if self._token_manager:
|
|
self._token_manager.close()
|