File size: 9,725 Bytes
e3e619b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 | """Google Antigravity SDK Agent Runner and Context Management.
Provides execution wrappers for executing Coding and Evaluator AI Agents using
the Google Antigravity SDK. Includes serialized working directory controls
and automatic local sandbox approvals.
"""
import asyncio
import contextlib
import logging
import os
from typing import Iterator
@contextlib.contextmanager
def working_directory(path: str | os.PathLike) -> Iterator[None]:
"""Safely and temporarily changes the working directory.
Guarantees restoration of the original CWD even in the event of failures.
Args:
path: Directory path to switch to.
Yields:
None.
"""
original_cwd = os.getcwd()
logging.debug("Switching working directory from %s to %s", original_cwd, path)
os.chdir(path)
try:
yield
finally:
logging.debug("Restoring working directory to %s", original_cwd)
os.chdir(original_cwd)
# Permitted tool allowlist for headless sandbox operations
ALLOWED_SANDBOX_TOOLS = {
# Reading tools
"view_file",
"read_file",
# File writing & editing tools
"replace_file_content",
"multi_replace_file_content",
"write_file",
"write_to_file",
# Command execution
"run_command",
}
# Registering global agent hooks for local sandbox tool calls
try:
from google.antigravity import Agent, LocalAgentConfig, hooks, policy
except ImportError:
Agent, LocalAgentConfig, hooks, policy = None, None, None, None
if hooks is not None:
@hooks.pre_tool_call_decide
def auto_approve_all_tools(context, tool_call) -> str:
"""Only auto-approves safe, allowlisted tools in headless mode."""
if tool_call.name in ALLOWED_SANDBOX_TOOLS:
logging.debug("Auto-approving allowlisted sandbox tool call: %s", tool_call.name)
return "PROCEED"
logging.warning("Rejecting non-allowlisted tool call: %s", tool_call.name)
return "REJECT"
class AgentRunnerError(Exception):
"""Raised when the AI Agent fails to run or complete execution loops."""
class AgentRunner:
"""Manages AI Agent setups and coordinates conversation execution loops."""
_cwd_lock: asyncio.Lock | None = None
def __init__(
self,
project_id: str,
location: str = "global",
model_name: str = "gemini-3.5-flash",
script_dir: str | None = None,
) -> None:
"""Initializes the runner with target Vertex AI details.
Args:
project_id: Target Google Cloud Platform Project ID.
location: Global endpoint location of Vertex AI services (default: "global").
model_name: Base LLM version string.
script_dir: Directory containing system/prompt markdown files.
"""
self.project_id = project_id
self.location = location or "global"
self.model_name = model_name
self.script_dir = script_dir or os.path.dirname(
os.path.abspath(__file__)
)
def _load_prompt_file(self, filename: str) -> str | None:
"""Helper to read a localized system instruction prompt markdown file.
Args:
filename: Name of the prompt file inside the script directory.
Returns:
The text content if file exists, else None.
"""
path = os.path.abspath(os.path.join(self.script_dir, filename))
if not path.startswith(os.path.abspath(self.script_dir)):
logging.warning("Path traversal attempt detected in prompt loading: %s", filename)
return None
if os.path.exists(path):
try:
with open(path, "r", encoding="utf-8") as f:
return f.read()
except IOError as e:
logging.warning(
"Failed to read prompt file '%s': %s", filename, e
)
return None
async def run_agent(
self,
role: str,
prompt: str,
repo_path: str,
system_prompt_file: str | None = None,
) -> str:
"""Launches and manages an asynchronous conversation with an Antigravity Agent.
Args:
role: Label representing the agent's role (e.g., 'Coding Agent').
prompt: User message prompt guiding the immediate task.
repo_path: Target directory root of the repository to execute in.
system_prompt_file: Optional filename of system prompt markdown.
Returns:
A reconstructed single text block combining thoughts and outputs.
Raises:
AgentRunnerError: If Agent fails to run or execution fails.
"""
if Agent is None:
raise AgentRunnerError("Google Antigravity SDK is not installed.")
logging.info("Initializing Agent '%s' inside %s", role, repo_path)
# Build fallback / configured system instructions
system_instructions = f"You are the {role}. You must complete the requested tasks in the workspace."
if system_prompt_file:
loaded_instructions = self._load_prompt_file(system_prompt_file)
if loaded_instructions:
system_instructions = loaded_instructions
logging.info(
"System prompt successfully loaded from %s",
system_prompt_file
)
else:
logging.warning(
"Requested system prompt file '%s' not found. Reverting to default instructions.",
system_prompt_file,
)
config = LocalAgentConfig(
vertex=True,
project=self.project_id,
location=self.location,
model=self.model_name,
system_instructions=system_instructions,
policies=[policy.allow_all()],
workspaces=[repo_path],
)
stdout_list: list[str] = []
thinking_list: list[str] = []
if AgentRunner._cwd_lock is None:
AgentRunner._cwd_lock = asyncio.Lock()
try:
# We change CWD to the repo workspace because the Antigravity SDK Agent
# interacts relative to the current working process directory.
# Since os.chdir is process-wide, we must serialize execution to prevent
# concurrent tasks from corrupting the CWD.
async with AgentRunner._cwd_lock:
with working_directory(repo_path):
async with Agent(config) as agent:
logging.info(
"[%s] Sending initial task prompt to conversation loop...",
role,
)
await agent.conversation.send(prompt)
step_contents: dict[int, str] = {}
step_thoughts: dict[int, str] = {}
printed_steps: set[tuple[int, str]] = set()
async for step in agent.conversation.receive_steps():
if step.content:
step_contents[step.step_index] = step.content
# Retrieve thoughts if available via standard properties
thinking = getattr(step, "thinking", None) or getattr(
step, "thinking_delta", None
)
if thinking:
step_thoughts[step.step_index] = str(thinking)
step_key = (step.step_index, str(step.status))
if step_key not in printed_steps:
printed_steps.add(step_key)
logging.info(
"[%s Step %s] Type: %s (Source: %s, Status: %s)",
role,
step.step_index,
step.type,
step.source,
step.status,
)
if step.content:
logging.info("[%s Content]: %s", role, step.content)
if thinking:
logging.debug("[%s Thinking]: %s", role, thinking)
if step.tool_calls:
for call in step.tool_calls:
logging.info(
"[%s Tool Call]: %s with args %s",
role,
call.name,
call.args,
)
# Accumulate outputs
for step_idx in sorted(step_contents.keys()):
stdout_list.append(step_contents[step_idx])
for step_idx in sorted(step_thoughts.keys()):
thinking_list.append(step_thoughts[step_idx])
full_output = "\n".join(stdout_list)
if thinking_list:
joined_thoughts = "\n".join(thinking_list)
full_output += f"\nThoughts:\n{joined_thoughts}"
logging.info("Agent '%s' execution completed successfully.", role)
return full_output
except Exception as e:
logging.exception("Failed to execute agent loop for role: %s", role)
raise AgentRunnerError(f"Agent '{role}' execution failed: {e}") from e
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