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https://github.com/bytedance/deer-flow.git
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feat: refine token usage display modes (#2329)
* feat: refine token usage display modes * docs: clarify token usage accounting semantics * fix: avoid duplicate subtask debug keys * style: format token usage tests * chore: address token attribution review feedback * Update test_token_usage_middleware.py * Update test_token_usage_middleware.py * chore: simplify token attribution fallback * fix token usage metadata follow-up handling --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
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YuJitang
@@ -1,31 +1,270 @@
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"""Middleware for logging LLM token usage."""
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"""Middleware for logging token usage and annotating step attribution."""
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from __future__ import annotations
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import logging
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from typing import override
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from collections import defaultdict
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from typing import Any, override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langchain.agents.middleware.todo import Todo
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from langchain_core.messages import AIMessage
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from langgraph.runtime import Runtime
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logger = logging.getLogger(__name__)
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TOKEN_USAGE_ATTRIBUTION_KEY = "token_usage_attribution"
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def _string_arg(value: Any) -> str | None:
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if isinstance(value, str):
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normalized = value.strip()
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return normalized or None
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return None
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def _normalize_todos(value: Any) -> list[Todo]:
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if not isinstance(value, list):
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return []
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normalized: list[Todo] = []
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for item in value:
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if not isinstance(item, dict):
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continue
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todo: Todo = {}
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content = _string_arg(item.get("content"))
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status = item.get("status")
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if content is not None:
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todo["content"] = content
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if status in {"pending", "in_progress", "completed"}:
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todo["status"] = status
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normalized.append(todo)
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return normalized
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def _todo_action_kind(previous: Todo | None, current: Todo) -> str:
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status = current.get("status")
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previous_content = previous.get("content") if previous else None
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current_content = current.get("content")
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if previous is None:
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if status == "completed":
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return "todo_complete"
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if status == "in_progress":
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return "todo_start"
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return "todo_update"
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if previous_content != current_content:
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return "todo_update"
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if status == "completed":
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return "todo_complete"
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if status == "in_progress":
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return "todo_start"
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return "todo_update"
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def _build_todo_actions(previous_todos: list[Todo], next_todos: list[Todo]) -> list[dict[str, Any]]:
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# This is the single source of truth for precise write_todos token
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# attribution. The frontend intentionally falls back to a generic
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# "Update to-do list" label when this metadata is missing or malformed.
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previous_by_content: dict[str, list[tuple[int, Todo]]] = defaultdict(list)
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matched_previous_indices: set[int] = set()
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for index, todo in enumerate(previous_todos):
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content = todo.get("content")
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if isinstance(content, str) and content:
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previous_by_content[content].append((index, todo))
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actions: list[dict[str, Any]] = []
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for index, todo in enumerate(next_todos):
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content = todo.get("content")
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if not isinstance(content, str) or not content:
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continue
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previous_match: Todo | None = None
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content_matches = previous_by_content.get(content)
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if content_matches:
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while content_matches and content_matches[0][0] in matched_previous_indices:
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content_matches.pop(0)
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if content_matches:
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previous_index, previous_match = content_matches.pop(0)
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matched_previous_indices.add(previous_index)
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if previous_match is None and index < len(previous_todos) and index not in matched_previous_indices:
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previous_match = previous_todos[index]
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matched_previous_indices.add(index)
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if previous_match is not None:
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previous_content = previous_match.get("content")
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previous_status = previous_match.get("status")
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if previous_content == content and previous_status == todo.get("status"):
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continue
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actions.append(
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{
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"kind": _todo_action_kind(previous_match, todo),
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"content": content,
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}
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)
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for index, todo in enumerate(previous_todos):
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if index in matched_previous_indices:
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continue
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content = todo.get("content")
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if not isinstance(content, str) or not content:
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continue
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actions.append(
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{
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"kind": "todo_remove",
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"content": content,
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}
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)
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return actions
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def _describe_tool_call(tool_call: dict[str, Any], todos: list[Todo]) -> list[dict[str, Any]]:
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name = _string_arg(tool_call.get("name")) or "unknown"
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args = tool_call.get("args") if isinstance(tool_call.get("args"), dict) else {}
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tool_call_id = _string_arg(tool_call.get("id"))
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if name == "write_todos":
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next_todos = _normalize_todos(args.get("todos"))
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actions = _build_todo_actions(todos, next_todos)
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if not actions:
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return [
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{
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"kind": "tool",
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"tool_name": name,
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"tool_call_id": tool_call_id,
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}
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]
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return [
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{
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**action,
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"tool_call_id": tool_call_id,
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}
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for action in actions
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]
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if name == "task":
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return [
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{
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"kind": "subagent",
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"description": _string_arg(args.get("description")),
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"subagent_type": _string_arg(args.get("subagent_type")),
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"tool_call_id": tool_call_id,
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}
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]
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if name in {"web_search", "image_search"}:
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query = _string_arg(args.get("query"))
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return [
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{
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"kind": "search",
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"tool_name": name,
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"query": query,
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"tool_call_id": tool_call_id,
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}
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]
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if name == "present_files":
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return [
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{
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"kind": "present_files",
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"tool_call_id": tool_call_id,
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}
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]
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if name == "ask_clarification":
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return [
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{
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"kind": "clarification",
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"tool_call_id": tool_call_id,
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}
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]
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return [
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{
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"kind": "tool",
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"tool_name": name,
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"description": _string_arg(args.get("description")),
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"tool_call_id": tool_call_id,
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}
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]
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def _infer_step_kind(message: AIMessage, actions: list[dict[str, Any]]) -> str:
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if actions:
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first_kind = actions[0].get("kind")
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if len(actions) == 1 and first_kind in {"todo_start", "todo_complete", "todo_update", "todo_remove"}:
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return "todo_update"
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if len(actions) == 1 and first_kind == "subagent":
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return "subagent_dispatch"
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return "tool_batch"
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if message.content:
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return "final_answer"
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return "thinking"
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def _build_attribution(message: AIMessage, todos: list[Todo]) -> dict[str, Any]:
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tool_calls = getattr(message, "tool_calls", None) or []
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actions: list[dict[str, Any]] = []
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current_todos = list(todos)
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for raw_tool_call in tool_calls:
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if not isinstance(raw_tool_call, dict):
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continue
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described_actions = _describe_tool_call(raw_tool_call, current_todos)
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actions.extend(described_actions)
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if raw_tool_call.get("name") == "write_todos":
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args = raw_tool_call.get("args") if isinstance(raw_tool_call.get("args"), dict) else {}
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current_todos = _normalize_todos(args.get("todos"))
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tool_call_ids: list[str] = []
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for tool_call in tool_calls:
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if not isinstance(tool_call, dict):
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continue
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tool_call_id = _string_arg(tool_call.get("id"))
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if tool_call_id is not None:
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tool_call_ids.append(tool_call_id)
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return {
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# Schema changes should remain additive where possible so older
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# frontends can ignore unknown fields and fall back safely.
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"version": 1,
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"kind": _infer_step_kind(message, actions),
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"shared_attribution": len(actions) > 1,
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"tool_call_ids": tool_call_ids,
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"actions": actions,
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}
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class TokenUsageMiddleware(AgentMiddleware):
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"""Logs token usage from model response usage_metadata."""
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"""Logs token usage from model responses and annotates the AI step."""
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@override
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def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
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return self._log_usage(state)
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@override
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async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
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return self._log_usage(state)
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def _log_usage(self, state: AgentState) -> None:
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def _apply(self, state: AgentState) -> dict | None:
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messages = state.get("messages", [])
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if not messages:
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return None
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last = messages[-1]
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if not isinstance(last, AIMessage):
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return None
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usage = getattr(last, "usage_metadata", None)
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if usage:
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logger.info(
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@@ -34,4 +273,22 @@ class TokenUsageMiddleware(AgentMiddleware):
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usage.get("output_tokens", "?"),
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usage.get("total_tokens", "?"),
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)
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return None
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todos = state.get("todos") or []
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attribution = _build_attribution(last, todos if isinstance(todos, list) else [])
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additional_kwargs = dict(getattr(last, "additional_kwargs", {}) or {})
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if additional_kwargs.get(TOKEN_USAGE_ATTRIBUTION_KEY) == attribution:
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return None
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additional_kwargs[TOKEN_USAGE_ATTRIBUTION_KEY] = attribution
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updated_msg = last.model_copy(update={"additional_kwargs": additional_kwargs})
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return {"messages": [updated_msg]}
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@override
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def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
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return self._apply(state)
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@override
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async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
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return self._apply(state)
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