mirror of
https://github.com/langchain-ai/deepagents.git
synced 2026-08-27 10:51:26 -04:00
Mason Daugherty
115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
"""Lightweight session statistics and token formatting utilities.
|
|
|
|
This module is intentionally kept free of heavy dependencies (no pydantic, no
|
|
config, no widget imports) so that `app.py` can import `SessionStats` and
|
|
`format_token_count` at module level without pulling in the full
|
|
`textual_adapter` dependency tree.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass, field
|
|
from typing import Literal
|
|
|
|
SpinnerStatus = Literal["Thinking", "Offloading"] | None
|
|
"""Valid spinner display states, or `None` to hide."""
|
|
|
|
|
|
@dataclass
|
|
class ModelStats:
|
|
"""Token stats for a single model within a session.
|
|
|
|
Attributes:
|
|
request_count: Number of LLM API requests made to this model.
|
|
input_tokens: Cumulative input tokens sent to this model.
|
|
output_tokens: Cumulative output tokens received from this model.
|
|
"""
|
|
|
|
request_count: int = 0
|
|
input_tokens: int = 0
|
|
output_tokens: int = 0
|
|
|
|
|
|
@dataclass
|
|
class SessionStats:
|
|
"""Stats accumulated over a single agent turn (or full session).
|
|
|
|
Attributes:
|
|
request_count: Total LLM API requests made (each chunk with
|
|
usage_metadata counts as one completed request).
|
|
input_tokens: Cumulative input tokens across all LLM requests.
|
|
output_tokens: Cumulative output tokens across all LLM requests.
|
|
wall_time_seconds: Wall-clock duration from stream start to end.
|
|
per_model: Per-model breakdown keyed by model name.
|
|
Populated only when `record_request` receives a non-empty
|
|
`model_name`. Empty dict means no named-model requests were
|
|
recorded; `print_usage_table` omits the model table in that case and
|
|
shows only the wall-time line (if applicable).
|
|
"""
|
|
|
|
request_count: int = 0
|
|
input_tokens: int = 0
|
|
output_tokens: int = 0
|
|
wall_time_seconds: float = 0.0
|
|
per_model: dict[str, ModelStats] = field(default_factory=dict)
|
|
|
|
def record_request(
|
|
self,
|
|
model_name: str,
|
|
input_toks: int,
|
|
output_toks: int,
|
|
) -> None:
|
|
"""Accumulate token counts for one completed LLM request.
|
|
|
|
Updates both the session totals and the per-model breakdown.
|
|
|
|
Args:
|
|
model_name: The model that served this request (used as the
|
|
per-model key). Pass an empty string to skip the per-model
|
|
breakdown for this request.
|
|
input_toks: Input tokens for this request.
|
|
output_toks: Output tokens for this request.
|
|
"""
|
|
self.request_count += 1
|
|
self.input_tokens += input_toks
|
|
self.output_tokens += output_toks
|
|
if model_name:
|
|
entry = self.per_model.setdefault(model_name, ModelStats())
|
|
entry.request_count += 1
|
|
entry.input_tokens += input_toks
|
|
entry.output_tokens += output_toks
|
|
|
|
def merge(self, other: SessionStats) -> None:
|
|
"""Merge another `SessionStats` into this one (mutates *self*).
|
|
|
|
Used to accumulate per-turn stats into a session-level total.
|
|
|
|
Args:
|
|
other: The stats to fold in.
|
|
"""
|
|
self.request_count += other.request_count
|
|
self.input_tokens += other.input_tokens
|
|
self.output_tokens += other.output_tokens
|
|
self.wall_time_seconds += other.wall_time_seconds
|
|
for model, ms in other.per_model.items():
|
|
entry = self.per_model.setdefault(model, ModelStats())
|
|
entry.request_count += ms.request_count
|
|
entry.input_tokens += ms.input_tokens
|
|
entry.output_tokens += ms.output_tokens
|
|
|
|
|
|
def format_token_count(count: int) -> str:
|
|
"""Format a token count into a human-readable short string.
|
|
|
|
Args:
|
|
count: Number of tokens.
|
|
|
|
Returns:
|
|
Formatted string like `'12.5K'`, `'1.2M'`, or `'500'`.
|
|
"""
|
|
if count >= 1_000_000: # noqa: PLR2004
|
|
return f"{count / 1_000_000:.1f}M"
|
|
if count >= 1000: # noqa: PLR2004
|
|
return f"{count / 1000:.1f}K"
|
|
return str(count)
|