mirror/nanobot
1
0
Fork 0
mirror of https://github.com/HKUDS/nanobot.git synced 2026-05-01 07:15:52 +00:00
Code Issues Packages Projects Releases Wiki Activity

refactor(agent): move usage logging to _LoopHook, simplify runner

Browse Source 
- Runner no longer creates an intermediate iter_usage dict; it passes
  raw_usage directly to context.usage for hooks to consume freely.
- Usage logging moves to _LoopHook.after_iteration() alongside other
  product-layer concerns (progress, tool hints).
- Runner only keeps the minimal accumulation needed for AgentRunResult.
- Removes loguru import from runner.py (no longer needed).
This commit is contained in:
chengyongru 2026-03-30 17:56:39 +08:00
parent 9c869d0bdf
commit 57b46bc520
2 changed files with 17 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
nanobot/agent/loop.py
View File

@ -257,6 +257,15 @@ class AgentLoop:
def finalize_content(self, context: AgentHookContext, content: str | None) -> str | None: def finalize_content(self, context: AgentHookContext, content: str | None) -> str | None:
return loop_self._strip_think(content) return loop_self._strip_think(content)
async def after_iteration(self, context: AgentHookContext) -> None:
u = context.usage or {}
logger.debug(
"LLM usage: prompt={} completion={} cached={}",
u.get("prompt_tokens", 0),
u.get("completion_tokens", 0),
u.get("cached_tokens", 0),
)
result = await self.runner.run(AgentRunSpec( result = await self.runner.run(AgentRunSpec(
initial_messages=initial_messages, initial_messages=initial_messages,
tools=self.tools, tools=self.tools,

30
nanobot/agent/runner.py
View File

@ -6,8 +6,6 @@ import asyncio
from dataclasses import dataclass, field from dataclasses import dataclass, field
from typing import Any from typing import Any
from loguru import logger
from nanobot.agent.hook import AgentHook, AgentHookContext from nanobot.agent.hook import AgentHook, AgentHookContext
from nanobot.agent.tools.registry import ToolRegistry from nanobot.agent.tools.registry import ToolRegistry
from nanobot.providers.base import LLMProvider, ToolCallRequest from nanobot.providers.base import LLMProvider, ToolCallRequest
@ -62,7 +60,7 @@ class AgentRunner:
messages = list(spec.initial_messages) messages = list(spec.initial_messages)
final_content: str | None = None final_content: str | None = None
tools_used: list[str] = [] tools_used: list[str] = []
usage = {"prompt_tokens": 0, "completion_tokens": 0, "cached_tokens": 0} usage: dict[str, int] = {}
error: str | None = None error: str | None = None
stop_reason = "completed" stop_reason = "completed"
tool_events: list[dict[str, str]] = [] tool_events: list[dict[str, str]] = []
@ -94,27 +92,15 @@ class AgentRunner:
response = await self.provider.chat_with_retry(**kwargs) response = await self.provider.chat_with_retry(**kwargs)
raw_usage = response.usage or {} raw_usage = response.usage or {}
iter_usage = { context.response = response
"prompt_tokens": int(raw_usage.get("prompt_tokens", 0) or 0), context.usage = raw_usage
"completion_tokens": int(raw_usage.get("completion_tokens", 0) or 0), context.tool_calls = list(response.tool_calls)
} # Accumulate standard fields into result usage.
# Pass through cached_tokens if present. usage["prompt_tokens"] = usage.get("prompt_tokens", 0) + int(raw_usage.get("prompt_tokens", 0) or 0)
usage["completion_tokens"] = usage.get("completion_tokens", 0) + int(raw_usage.get("completion_tokens", 0) or 0)
cached = raw_usage.get("cached_tokens") cached = raw_usage.get("cached_tokens")
if cached: if cached:
iter_usage["cached_tokens"] = int(cached) usage["cached_tokens"] = usage.get("cached_tokens", 0) + int(cached)
usage["prompt_tokens"] += iter_usage["prompt_tokens"]
usage["completion_tokens"] += iter_usage["completion_tokens"]
if "cached_tokens" in iter_usage:
usage["cached_tokens"] = usage.get("cached_tokens", 0) + iter_usage["cached_tokens"]
context.response = response
context.usage = iter_usage
logger.debug(
"LLM usage: prompt={} completion={} cached={}",
iter_usage["prompt_tokens"],
iter_usage["completion_tokens"],
iter_usage.get("cached_tokens", 0),
)
context.tool_calls = list(response.tool_calls)
if response.has_tool_calls: if response.has_tool_calls:
if hook.wants_streaming(): if hook.wants_streaming():