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FusionAGI/fusionagi/tools/runner.py
Devin AI 445865e429
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fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py

All 276 tests pass. 0 ruff errors. 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:48:37 +00:00

223 lines
7.8 KiB
Python

"""Safe runner: invoke tool with timeout, input validation, and failure handling; log for replay."""
import time
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from fusionagi.governance.audit_log import AuditLog
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import TimeoutError as FuturesTimeoutError
from typing import Any
from fusionagi._logger import logger
from fusionagi.tools.registry import ToolDef
class ToolValidationError(Exception):
"""Raised when tool arguments fail validation."""
def __init__(self, tool_name: str, message: str, details: dict[str, Any] | None = None):
self.tool_name = tool_name
self.details = details or {}
super().__init__(f"Tool {tool_name}: {message}")
def validate_args(tool: ToolDef, args: dict[str, Any]) -> tuple[bool, str]:
"""
Validate arguments against tool's JSON schema.
Returns:
Tuple of (is_valid, error_message). error_message is empty if valid.
"""
schema = tool.parameters_schema
if not schema:
return True, ""
# Basic JSON schema validation (without external dependency)
schema_type = schema.get("type", "object")
if schema_type != "object":
return True, "" # Only validate object schemas
properties = schema.get("properties", {})
required = schema.get("required", [])
# Check required fields
for field in required:
if field not in args:
return False, f"Missing required argument: {field}"
# Check types of provided fields
for field, value in args.items():
if field not in properties:
# Allow extra fields by default (additionalProperties: true is common)
continue
prop_schema = properties[field]
prop_type = prop_schema.get("type")
if prop_type is None:
continue
# Type checking
type_valid = True
if prop_type == "string":
type_valid = isinstance(value, str)
elif prop_type == "integer":
type_valid = isinstance(value, int) and not isinstance(value, bool)
elif prop_type == "number":
type_valid = isinstance(value, (int, float)) and not isinstance(value, bool)
elif prop_type == "boolean":
type_valid = isinstance(value, bool)
elif prop_type == "array":
type_valid = isinstance(value, list)
elif prop_type == "object":
type_valid = isinstance(value, dict)
elif prop_type == "null":
type_valid = value is None
if not type_valid:
return False, f"Argument '{field}' must be of type {prop_type}, got {type(value).__name__}"
# String constraints
if prop_type == "string" and isinstance(value, str):
min_len = prop_schema.get("minLength")
max_len = prop_schema.get("maxLength")
pattern = prop_schema.get("pattern")
if min_len is not None and len(value) < min_len:
return False, f"Argument '{field}' must be at least {min_len} characters"
if max_len is not None and len(value) > max_len:
return False, f"Argument '{field}' must be at most {max_len} characters"
if pattern:
import re
if not re.match(pattern, value):
return False, f"Argument '{field}' does not match pattern: {pattern}"
# Number constraints
if prop_type in ("integer", "number") and isinstance(value, (int, float)):
minimum = prop_schema.get("minimum")
maximum = prop_schema.get("maximum")
exclusive_min = prop_schema.get("exclusiveMinimum")
exclusive_max = prop_schema.get("exclusiveMaximum")
if minimum is not None and value < minimum:
return False, f"Argument '{field}' must be >= {minimum}"
if maximum is not None and value > maximum:
return False, f"Argument '{field}' must be <= {maximum}"
if exclusive_min is not None and value <= exclusive_min:
return False, f"Argument '{field}' must be > {exclusive_min}"
if exclusive_max is not None and value >= exclusive_max:
return False, f"Argument '{field}' must be < {exclusive_max}"
# Enum constraint
enum = prop_schema.get("enum")
if enum is not None and value not in enum:
return False, f"Argument '{field}' must be one of: {enum}"
return True, ""
def run_tool(
tool: ToolDef,
args: dict[str, Any],
timeout_seconds: float | None = None,
validate: bool = True,
) -> tuple[Any, dict[str, Any]]:
"""
Invoke tool.fn(args) with optional validation and timeout.
Args:
tool: The tool definition to execute.
args: Arguments to pass to the tool function.
timeout_seconds: Override timeout (uses tool.timeout_seconds if None).
validate: Whether to validate args against tool's schema (default True).
Returns:
Tuple of (result, log_entry). On error, result is None and log_entry contains error.
"""
timeout = timeout_seconds if timeout_seconds is not None else tool.timeout_seconds
start = time.monotonic()
log_entry: dict[str, Any] = {
"tool": tool.name,
"args": args,
"result": None,
"error": None,
"duration_seconds": None,
"validated": validate,
}
# Validate arguments before execution
if validate:
is_valid, error_msg = validate_args(tool, args)
if not is_valid:
log_entry["error"] = f"Validation error: {error_msg}"
log_entry["duration_seconds"] = round(time.monotonic() - start, 3)
logger.warning(
"Tool validation failed",
extra={"tool": tool.name, "error": error_msg},
)
return None, log_entry
def _invoke() -> Any:
return tool.fn(**args)
try:
with ThreadPoolExecutor(max_workers=1) as ex:
fut = ex.submit(_invoke)
result = fut.result(timeout=timeout if timeout and timeout > 0 else None)
log_entry["result"] = result
logger.debug(
"Tool executed successfully",
extra={"tool": tool.name, "duration": log_entry.get("duration_seconds")},
)
return result, log_entry
except FuturesTimeoutError:
log_entry["error"] = f"Tool {tool.name} timed out after {timeout}s"
logger.warning(
"Tool timed out",
extra={"tool": tool.name, "timeout": timeout},
)
return None, log_entry
except Exception as e:
log_entry["error"] = str(e)
logger.error(
"Tool execution failed",
extra={"tool": tool.name, "error": str(e), "error_type": type(e).__name__},
)
return None, log_entry
finally:
log_entry["duration_seconds"] = round(time.monotonic() - start, 3)
def run_tool_with_audit(
tool: ToolDef,
args: dict[str, Any],
audit_log: "AuditLog",
actor: str = "system",
task_id: str | None = None,
timeout_seconds: float | None = None,
validate: bool = True,
) -> tuple[Any, dict[str, Any]]:
"""
Invoke tool and log to AuditLog.
Sanitizes args in log (e.g. truncate long values).
"""
from fusionagi.schemas.audit import AuditEventType
sanitized = {}
for k, v in args.items():
if isinstance(v, str) and len(v) > 200:
sanitized[k] = v[:200] + "..."
else:
sanitized[k] = v
result, log_entry = run_tool(tool, args, timeout_seconds, validate)
audit_log.append(
AuditEventType.TOOL_CALL,
actor,
action=f"tool:{tool.name}",
task_id=task_id,
payload={"tool": tool.name, "args": sanitized, "error": log_entry.get("error")},
outcome="success" if result is not None else "failure",
)
return result, log_entry