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oliver
4a23b715a2
本次迁移将网关/通道/工具/技能/脚本与协议资源集中到新结构，统一路径常量与脚本转发机制，减少顶层噪音并保证运行与测试行为一致。 Made-with: Cursor
630 lines
27 KiB
Python
630 lines
27 KiB
Python
from __future__ import annotations
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"""Agent 工具执行模块。
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本模块把“工具执行（校验/并发/落库/回写）”从 `Agent.run_turn` 中下沉出来，
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以便被单 Agent 与编排器（manager/specialist）复用。
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"""
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import json
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import logging
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import time
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import os
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import threading
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from concurrent.futures import TimeoutError as FuturesTimeoutError
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from dataclasses import dataclass
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from typing import Any, Callable, Optional
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from oclaw.platform.persistence.sqlite_store import SqliteStore
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from oclaw.runtime.tools.base import ToolRegistry
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from oclaw.platform.llm.chat_models import LLMToolCall
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from oclaw.runtime.tools.tool_validation import validate_tool_arguments
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from oclaw.runtime.tools.experts.workspace.workspace_base import workspace_path_access_scope
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logger = logging.getLogger(__name__)
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_TOOL_ERROR_MAP = {
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"tool_timeout_or_failed": "tool_timeout_or_failed",
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}
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_SQL_REPLAY_COMPACT_TOOL_NAMES = {
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"query_tabular_attachment",
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"run_tabular_sql",
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"analyze_tabular_attachment_full_scan",
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}
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def normalize_tool_result(result: Any) -> dict[str, Any]:
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if isinstance(result, dict):
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out = dict(result)
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if "ok" in out:
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out["ok"] = bool(out.get("ok"))
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else:
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# Backward compatibility: many lightweight tools return payload-only dicts.
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# Treat those as success unless they explicitly carry error semantics.
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has_error = bool(str(out.get("error_code") or "").strip() or str(out.get("error") or "").strip())
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out["ok"] = not has_error
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else:
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out = {"ok": False, "error": "tool_result_not_dict", "data": result}
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if not out["ok"]:
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raw_ec = str(out.get("error_code") or "").strip()
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raw_err = str(out.get("error") or "").strip()
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if not raw_ec:
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out["error_code"] = _TOOL_ERROR_MAP.get(raw_err, "tool_failed")
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return out
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def tool_llm_message_max_chars() -> int:
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raw = str(os.getenv("AIA_TOOL_LLM_MESSAGE_MAX_CHARS") or "").strip()
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if raw.isdigit():
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n = int(raw)
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if n == 0:
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return 0
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return max(4096, min(n, 500_000))
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return 0
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def tool_history_summary_after_calls() -> int:
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raw = str(os.getenv("AIA_TOOL_HISTORY_SUMMARY_AFTER_CALLS") or "").strip()
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if raw.isdigit():
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return max(0, min(int(raw), 200))
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# Default: when the same tool is called >= 3 times in one turn, keep history compact.
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return 3
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def _json_blob_size(obj: Any) -> int:
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try:
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return len(json.dumps(obj, ensure_ascii=False, default=str))
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except Exception:
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return len(repr(obj))
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def _estimate_observed_rows(result: dict[str, Any]) -> int:
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if not isinstance(result, dict):
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return 0
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try:
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rr = result.get("rows_returned")
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if isinstance(rr, (int, float)):
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return max(0, int(rr))
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except Exception:
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pass
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rows = result.get("rows")
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if isinstance(rows, list):
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return max(0, len(rows))
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nested = result.get("result")
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if isinstance(nested, dict):
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nrows = nested.get("rows")
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if isinstance(nrows, list):
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return max(0, len(nrows))
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return 0
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def _deep_truncate_for_llm(obj: Any, *, max_str: int, max_list: int) -> Any:
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if isinstance(obj, dict):
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return {str(k): _deep_truncate_for_llm(v, max_str=max_str, max_list=max_list) for k, v in obj.items()}
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if isinstance(obj, list):
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items = obj
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omitted = 0
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if len(items) > max_list:
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omitted = len(items) - max_list
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items = items[:max_list]
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out: list[Any] = [_deep_truncate_for_llm(x, max_str=max_str, max_list=max_list) for x in items]
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if omitted:
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out.append(f"…({omitted} more list items omitted)")
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return out
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if isinstance(obj, str) and len(obj) > max_str:
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return obj[:max_str] + "\n...<truncated>"
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return obj
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def partition_tool_use_batches(
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tool_uses: list[LLMToolCall],
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registry: ToolRegistry,
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) -> list[list[LLMToolCall]]:
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"""Split tool uses into ordered batches (cc-mini ``Engine.submit`` scheduling).
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Consecutive tools whose ``ToolSpec.is_read_only()`` is true are merged into one batch
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and may run in parallel when the batch length is greater than one. Any other tool
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starts a new batch (typically length 1), which runs sequentially relative to other
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batches and uses a single worker within the batch.
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"""
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batches: list[tuple[bool, list[LLMToolCall]]] = []
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for tc in tool_uses:
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spec = registry.get(tc.name)
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is_concurrent = bool(spec and spec.is_read_only())
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if batches and batches[-1][0] == is_concurrent and is_concurrent:
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batches[-1][1].append(tc)
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else:
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batches.append((is_concurrent, [tc]))
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return [chunk for _, chunk in batches]
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def truncate_tool_result_for_llm_messages(result: dict[str, Any], *, max_chars: int | None = None) -> dict[str, Any]:
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"""Return a copy safe to put in ``role=tool`` ``content`` so the next LLM request stays under provider limits."""
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cap = tool_llm_message_max_chars() if max_chars is None else max(0, min(int(max_chars), 500_000))
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if cap == 0:
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return result if isinstance(result, dict) else {"ok": False, "error": "tool_result_not_dict", "data": result}
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if not isinstance(result, dict):
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return {"ok": False, "error": "tool_result_not_dict", "payload_type": type(result).__name__}
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if _json_blob_size(result) <= cap:
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return result
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orig_files_n = len(result["files"]) if isinstance(result.get("files"), list) else 0
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pairs = (
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(12_000, 800),
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(8000, 500),
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(4000, 300),
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(2000, 200),
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(1200, 120),
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(800, 80),
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(500, 50),
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(400, 40),
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)
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for max_str, max_list in pairs:
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slim = _deep_truncate_for_llm(result, max_str=max_str, max_list=max_list)
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if not isinstance(slim, dict):
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slim = {"ok": bool(result.get("ok")), "payload": slim}
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if _json_blob_size(slim) <= cap:
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slim = dict(slim)
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slim["_truncated_for_llm"] = True
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if orig_files_n and isinstance(slim.get("files"), list):
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kept = sum(1 for x in slim["files"] if isinstance(x, str))
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if kept < orig_files_n:
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slim["files_total"] = orig_files_n
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slim["files_omitted"] = orig_files_n - kept
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return slim
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return {
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"ok": bool(result.get("ok")),
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"_truncated_for_llm": True,
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"hint": (
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"Tool output exceeded model message size limits. "
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"Narrow the glob, lower max_results, or list a subdirectory. / "
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"工具输出超过模型单条消息限制，请缩小列举范围或降低 max_results。"
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),
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}
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@dataclass(frozen=True)
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class ToolExecutionConfig:
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max_workers: int = 8
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@dataclass(frozen=True)
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class ToolExecutionContext:
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store: SqliteStore
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tools: ToolRegistry
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session_id: str
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lang: str = "zh"
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user_text: str = ""
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specialist: str = ""
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task_kind: str = ""
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policy_engine: Any | None = None
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trace_id: str | None = None
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parent_span_id: str | None = None
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#: When ``session_id`` is a specialist temp chat row (no ``ui_session_owner``), use the user's UI session for ``extra_roots`` / allowlist.
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workspace_owner_session_id: str | None = None
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#: If ``get_ui_session_owner`` fails, load allowlist for this (tenant, user) from the HTTP/gateway request (``metadata``).
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path_policy_tenant_id: str | None = None
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path_policy_user_id: str | None = None
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turn_uuid: str | None = None
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class ToolExecutor:
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"""执行一组 tool uses，并把结果写回 store。"""
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def __init__(self, *, config: ToolExecutionConfig | None = None):
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self.config = config or ToolExecutionConfig()
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def _execute_tool(self, ctx: ToolExecutionContext, tc: LLMToolCall) -> tuple[dict[str, Any], int]:
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t0 = time.perf_counter()
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tool = ctx.tools.get(tc.name)
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if not tool:
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msg = f"Unregistered tool: {tc.name}" if ctx.lang.startswith("en") else f"未注册的工具: {tc.name}"
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return {"ok": False, "error_code": "tool_not_registered", "error": msg}, int((time.perf_counter() - t0) * 1000)
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ok, v_err = validate_tool_arguments(tool.parameters, tc.arguments)
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if not ok:
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msg = f"Invalid arguments: {v_err}" if ctx.lang.startswith("en") else f"参数不合法: {v_err}"
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return {"ok": False, "error_code": "tool_invalid_arguments", "error": msg}, int((time.perf_counter() - t0) * 1000)
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try:
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timeout_s = getattr(tool, "timeout_s", None)
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# Default timeout for plugin tools if not specified.
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if timeout_s is None and "plugin" in getattr(tool, "tags", frozenset()):
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timeout_s = 30.0
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def _call() -> Any:
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with workspace_path_access_scope(
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ctx.store,
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ctx.session_id,
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owner_fallback_session_id=ctx.workspace_owner_session_id,
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allowlist_tenant_id=ctx.path_policy_tenant_id,
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allowlist_user_id=ctx.path_policy_user_id,
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):
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return tool.handler(tc.arguments)
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if isinstance(timeout_s, (int, float)) and float(timeout_s) > 0:
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ex = ThreadPoolExecutor(max_workers=1)
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fut = ex.submit(_call)
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try:
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result = fut.result(timeout=float(timeout_s))
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except FuturesTimeoutError as e:
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try:
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fut.cancel()
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except Exception:
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pass
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try:
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ex.shutdown(wait=False, cancel_futures=True)
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except Exception:
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ex.shutdown(wait=False)
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return {"ok": False, "error_code": "tool_timeout_or_failed", "error": "tool_timeout_or_failed", "detail": f"{type(e).__name__}: {e}"}, int(
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(time.perf_counter() - t0) * 1000
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)
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except Exception as e:
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try:
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ex.shutdown(wait=False, cancel_futures=True)
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except Exception:
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ex.shutdown(wait=False)
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return {"ok": False, "error_code": "tool_timeout_or_failed", "error": "tool_timeout_or_failed", "detail": f"{type(e).__name__}: {e}"}, int(
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(time.perf_counter() - t0) * 1000
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)
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else:
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try:
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ex.shutdown(wait=False, cancel_futures=True)
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except Exception:
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ex.shutdown(wait=False)
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else:
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result = _call()
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return normalize_tool_result(result), int((time.perf_counter() - t0) * 1000)
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except Exception as e:
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if ctx.lang.startswith("en"):
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err = {"ok": False, "error_code": "tool_execution_error", "error": f"Tool execution error: {type(e).__name__}: {e}"}
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else:
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err = {"ok": False, "error_code": "tool_execution_error", "error": f"工具执行异常: {type(e).__name__}: {e}"}
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return normalize_tool_result(err), int((time.perf_counter() - t0) * 1000)
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@staticmethod
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def _json_dumps_safe(obj: Any) -> str:
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try:
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return json.dumps(obj, ensure_ascii=False, default=str)
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except (TypeError, ValueError):
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return json.dumps({"ok": False, "error": "tool result is not JSON-serializable"}, ensure_ascii=False)
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def execute_tool_uses(
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self,
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*,
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ctx: ToolExecutionContext,
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assistant_msg_id: int,
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tool_uses: list[LLMToolCall],
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on_tool_ui: Optional[Callable[[str, dict[str, Any]], None]] = None,
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should_stop: Optional[Callable[[], bool]] = None,
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signature_budget: int = 2,
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) -> tuple[list[dict[str, Any]], dict[str, tuple[dict[str, Any], int]]]:
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"""执行并回写 tool messages。
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Returns:
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- tool_messages: 用于写入对话 history 的 `role=tool` 消息 payload 列表（与 tool_uses 顺序一致）
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- results_by_id: tool_call_id -> (result_dict, duration_ms)
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"""
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def _check_stop() -> None:
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if should_stop and should_stop():
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raise RuntimeError("generation interrupted by user")
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def _trace(event_type: str, payload: dict[str, Any]) -> None:
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if not ctx.trace_id:
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return
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try:
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from oclaw.runtime.orchestration.trace import new_span_id
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ctx.store.add_trace_event(
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session_id=ctx.session_id,
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trace_id=str(ctx.trace_id),
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span_id=new_span_id(),
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parent_span_id=ctx.parent_span_id,
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event_type=str(event_type),
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payload=dict(payload or {}),
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)
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except Exception:
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pass
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def _load_turn_tool_stats() -> tuple[dict[str, int], dict[str, int]]:
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counts: dict[str, int] = {}
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observed_rows: dict[str, int] = {}
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if not str(ctx.turn_uuid or "").strip():
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return counts, observed_rows
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try:
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rows = ctx.store.get_messages(session_id=ctx.session_id, limit=500)
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except Exception:
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return counts, observed_rows
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for m in rows or []:
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if str(getattr(m, "role", "") or "") != "tool":
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continue
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if str(getattr(m, "turn_uuid", "") or "") != str(ctx.turn_uuid or ""):
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continue
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raw_tc = getattr(m, "tool_calls", None)
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name = ""
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if isinstance(raw_tc, str):
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try:
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parsed = json.loads(raw_tc)
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except Exception:
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parsed = None
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else:
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parsed = raw_tc
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if isinstance(parsed, dict):
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name = str(parsed.get("name") or "").strip()
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if not name:
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continue
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counts[name] = int(counts.get(name, 0)) + 1
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try:
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raw_content = str(getattr(m, "content", "") or "")
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payload = json.loads(raw_content) if raw_content else {}
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except Exception:
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payload = {}
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if isinstance(payload, dict):
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observed_rows[name] = int(observed_rows.get(name, 0)) + int(
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_estimate_observed_rows(payload)
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or payload.get("_tool_observed_rows_this_call")
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or 0
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)
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return counts, observed_rows
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def _compact_tool_result_for_history(
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*,
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tool_name: str,
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result: dict[str, Any],
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call_index: int,
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threshold: int,
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observed_rows_this_call: int,
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observed_rows_cumulative_in_turn: int,
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) -> dict[str, Any]:
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out: dict[str, Any] = {
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"ok": bool(result.get("ok")),
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"_history_compacted": True,
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"_history_compact_reason": "repeated_tool_calls_in_turn",
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"tool_name": str(tool_name or ""),
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"call_index_in_turn_for_tool": int(call_index),
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"compact_threshold": int(threshold),
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"_tool_observed_rows_this_call": int(observed_rows_this_call),
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"_tool_observed_rows_cumulative_in_turn": int(observed_rows_cumulative_in_turn),
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"result_keys": sorted(list(result.keys()))[:30],
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"result_bytes": int(_json_blob_size(result)),
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"hint": (
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"Repeated tool calls in this turn were compacted in chat history to avoid context bloat. "
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"Full payload remains in tool logs."
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),
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"audit_note": (
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"History is compacted by system optimization. If more detail is needed, continue querying "
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"with the same SQL/tool parameters from this turn."
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),
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}
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for key in ("error_code", "error", "rows_returned", "limit", "table_id", "engine"):
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if key in result:
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out[key] = result.get(key)
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for key in ("input_sql", "executed_sql"):
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v = str(result.get(key) or "").strip()
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if v:
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out[key] = v[:1200]
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guard = result.get("sql_guard")
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if isinstance(guard, dict):
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out["sql_guard"] = {
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"readonly_enforced": bool(guard.get("readonly_enforced")),
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"auto_limit_applied": bool(guard.get("auto_limit_applied")),
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"result_row_cap": int(guard.get("result_row_cap") or 0),
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}
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return out
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_check_stop()
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if not tool_uses:
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return [], {}
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history_summary_threshold = int(tool_history_summary_after_calls())
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turn_tool_name_counts, turn_tool_observed_rows = _load_turn_tool_stats()
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local_turn_tool_name_counts: dict[str, int] = {}
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local_turn_tool_observed_rows: dict[str, int] = {}
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local_turn_written_tool_msgs: dict[str, list[dict[str, Any]]] = {}
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results_by_id: dict[str, tuple[dict[str, Any], int]] = {}
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runnable_tool_uses: list[LLMToolCall] = []
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sig_seen: dict[str, int] = {}
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budget = max(1, min(int(signature_budget or 2), 8))
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for tc in tool_uses:
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sig = f"{tc.name}:{self._json_dumps_safe(dict(tc.arguments or {}))}"
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count = int(sig_seen.get(sig, 0))
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if count >= budget:
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results_by_id[tc.id] = (
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{
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"ok": False,
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"error_code": "tool_loop_guard",
|
||
"error": f"tool loop guard triggered for signature: {tc.name}",
|
||
},
|
||
0,
|
||
)
|
||
_trace(
|
||
"tool_loop_guard",
|
||
{
|
||
"tool_name": tc.name,
|
||
"signature": sig[:300],
|
||
"budget": budget,
|
||
},
|
||
)
|
||
continue
|
||
sig_seen[sig] = count + 1
|
||
runnable_tool_uses.append(tc)
|
||
|
||
for batch in partition_tool_use_batches(runnable_tool_uses, ctx.tools):
|
||
_check_stop()
|
||
_trace(
|
||
"tool_batch_started",
|
||
{
|
||
"batch_size": len(batch),
|
||
"tool_names": [str(getattr(x, "name", "") or "") for x in batch],
|
||
},
|
||
)
|
||
if len(batch) > 1:
|
||
workers = min(int(self.config.max_workers), len(batch))
|
||
with ThreadPoolExecutor(max_workers=workers) as ex:
|
||
fut_to_tc = {ex.submit(self._execute_tool, ctx, tc): tc for tc in batch}
|
||
for fut in as_completed(fut_to_tc):
|
||
tc = fut_to_tc[fut]
|
||
results_by_id[tc.id] = fut.result()
|
||
else:
|
||
for tc in batch:
|
||
results_by_id[tc.id] = self._execute_tool(ctx, tc)
|
||
_trace(
|
||
"tool_batch_finished",
|
||
{
|
||
"batch_size": len(batch),
|
||
"tool_names": [str(getattr(x, "name", "") or "") for x in batch],
|
||
},
|
||
)
|
||
|
||
tool_messages: list[dict[str, Any]] = []
|
||
for tc in tool_uses:
|
||
_check_stop()
|
||
_trace(
|
||
"tool_called",
|
||
{
|
||
"tool_name": tc.name,
|
||
"arguments": tc.arguments,
|
||
"arguments_bytes": _json_blob_size(tc.arguments),
|
||
},
|
||
)
|
||
result, duration_ms = results_by_id[tc.id]
|
||
result = normalize_tool_result(result)
|
||
logger.info(
|
||
"tool_runtime tool session=%s name=%s duration_ms=%d ok=%s",
|
||
ctx.session_id[:12],
|
||
tc.name,
|
||
duration_ms,
|
||
result.get("ok") if isinstance(result, dict) else None,
|
||
)
|
||
t_db1 = time.perf_counter()
|
||
ctx.store.add_tool_log(
|
||
session_id=ctx.session_id,
|
||
tool_name=tc.name,
|
||
args=tc.arguments,
|
||
result=result,
|
||
specialist=ctx.specialist,
|
||
duration_ms=duration_ms,
|
||
)
|
||
tool_log_write_ms = int((time.perf_counter() - t_db1) * 1000)
|
||
# Full payload stays in tool_log; chat history must stay under provider per-message limits.
|
||
t_trunc = time.perf_counter()
|
||
observed_rows_this_call = int(_estimate_observed_rows(result))
|
||
result_for_llm = truncate_tool_result_for_llm_messages(result)
|
||
should_compact_history = tc.name in _SQL_REPLAY_COMPACT_TOOL_NAMES
|
||
if history_summary_threshold > 0 and should_compact_history:
|
||
prior = int(turn_tool_name_counts.get(tc.name, 0))
|
||
current = int(local_turn_tool_name_counts.get(tc.name, 0))
|
||
call_index = prior + current + 1
|
||
prior_rows = int(turn_tool_observed_rows.get(tc.name, 0))
|
||
current_rows = int(local_turn_tool_observed_rows.get(tc.name, 0))
|
||
observed_rows_cumulative_in_turn = prior_rows + current_rows + observed_rows_this_call
|
||
if call_index >= history_summary_threshold:
|
||
result_for_llm = _compact_tool_result_for_history(
|
||
tool_name=tc.name,
|
||
result=result,
|
||
call_index=call_index,
|
||
threshold=history_summary_threshold,
|
||
observed_rows_this_call=observed_rows_this_call,
|
||
observed_rows_cumulative_in_turn=observed_rows_cumulative_in_turn,
|
||
)
|
||
local_turn_tool_name_counts[tc.name] = current + 1
|
||
local_turn_tool_observed_rows[tc.name] = current_rows + observed_rows_this_call
|
||
trunc_ms = int((time.perf_counter() - t_trunc) * 1000)
|
||
tool_content = self._json_dumps_safe(result_for_llm)
|
||
t_db2 = time.perf_counter()
|
||
msg_row = ctx.store.add_message(
|
||
session_id=ctx.session_id,
|
||
role="tool",
|
||
content=tool_content,
|
||
tool_calls={"tool_call_id": tc.id, "name": tc.name, "assistant_message_id": assistant_msg_id},
|
||
turn_uuid=ctx.turn_uuid,
|
||
event_type="tool_result",
|
||
event_payload={"tool_name": tc.name, "observed_rows": int(observed_rows_this_call)},
|
||
)
|
||
tool_msg_write_ms = int((time.perf_counter() - t_db2) * 1000)
|
||
tool_messages.append({"role": "tool", "tool_call_id": tc.id, "content": tool_content, "name": tc.name})
|
||
tool_messages_idx = len(tool_messages) - 1
|
||
call_index_for_tool = int(turn_tool_name_counts.get(tc.name, 0)) + int(local_turn_tool_name_counts.get(tc.name, 0))
|
||
local_turn_written_tool_msgs.setdefault(tc.name, []).append(
|
||
{
|
||
"message_id": int(getattr(msg_row, "id", 0) or 0),
|
||
"tool_messages_idx": int(tool_messages_idx),
|
||
"result": dict(result or {}),
|
||
"observed_rows": int(observed_rows_this_call),
|
||
"call_index": int(call_index_for_tool),
|
||
"compacted": bool(isinstance(result_for_llm, dict) and result_for_llm.get("_history_compacted")),
|
||
}
|
||
)
|
||
# When threshold is reached for one SQL tool in the turn, retro-compact earlier same-tool tool messages too.
|
||
if history_summary_threshold > 0 and should_compact_history and call_index_for_tool >= history_summary_threshold:
|
||
running_rows = int(turn_tool_observed_rows.get(tc.name, 0))
|
||
entries = list(local_turn_written_tool_msgs.get(tc.name) or [])
|
||
for ent in entries:
|
||
running_rows += int(ent.get("observed_rows") or 0)
|
||
compacted_payload = _compact_tool_result_for_history(
|
||
tool_name=tc.name,
|
||
result=dict(ent.get("result") or {}),
|
||
call_index=int(ent.get("call_index") or 0),
|
||
threshold=history_summary_threshold,
|
||
observed_rows_this_call=int(ent.get("observed_rows") or 0),
|
||
observed_rows_cumulative_in_turn=int(running_rows),
|
||
)
|
||
compacted_content = self._json_dumps_safe(compacted_payload)
|
||
if not bool(ent.get("compacted")):
|
||
try:
|
||
ctx.store.update_message_content(
|
||
session_id=ctx.session_id,
|
||
message_id=int(ent.get("message_id") or 0),
|
||
content=compacted_content,
|
||
event_payload={"tool_name": tc.name, "observed_rows": int(ent.get("observed_rows") or 0)},
|
||
)
|
||
except Exception:
|
||
pass
|
||
ent["compacted"] = True
|
||
ti = int(ent.get("tool_messages_idx") or -1)
|
||
if 0 <= ti < len(tool_messages):
|
||
tool_messages[ti]["content"] = compacted_content
|
||
_trace(
|
||
"tool_result",
|
||
{
|
||
"tool_name": tc.name,
|
||
"duration_ms": duration_ms,
|
||
"ok": bool(result.get("ok")) if isinstance(result, dict) else None,
|
||
"error_code": str(result.get("error_code") or "") if isinstance(result, dict) else "",
|
||
"result_bytes": _json_blob_size(result),
|
||
"result_for_llm_bytes": len(tool_content or ""),
|
||
"tool_log_write_ms": tool_log_write_ms,
|
||
"tool_message_write_ms": tool_msg_write_ms,
|
||
"truncate_ms": trunc_ms,
|
||
"active_threads": int(threading.active_count()),
|
||
},
|
||
)
|
||
if on_tool_ui:
|
||
truncated_for_llm = bool(
|
||
isinstance(result_for_llm, dict) and result_for_llm.get("_truncated_for_llm")
|
||
)
|
||
payload = {
|
||
"name": tc.name,
|
||
"result": result,
|
||
"llm_wire": {
|
||
"truncated_for_llm": truncated_for_llm,
|
||
"max_chars": int(tool_llm_message_max_chars()),
|
||
"result_bytes": int(_json_blob_size(result)),
|
||
"result_for_llm_bytes": int(len(tool_content or "")),
|
||
"truncate_ms": int(trunc_ms),
|
||
},
|
||
}
|
||
on_tool_ui("tool_use_result", payload)
|
||
return tool_messages, results_by_id
|
||
|
||
__all__ = [
|
||
"ToolExecutionConfig",
|
||
"ToolExecutionContext",
|
||
"ToolExecutor",
|
||
"normalize_tool_result",
|
||
"partition_tool_use_batches",
|
||
"tool_llm_message_max_chars",
|
||
"truncate_tool_result_for_llm_messages",
|
||
]
|