mirror of
https://github.com/HKUDS/nanobot.git
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refactor(agent): surgical extraction from AgentRunner.run()
Extract 3 focused helpers while keeping the iteration control flow inline and readable: - _prepare_context(ctx) – context governance pipeline (orphan cleanup, backfill, microcompact, budget, snip) - _try_drain_injections(ctx, ...) – simplified signature using RunContext + bool return (was tuple[bool, int]) - _terminate_with_error(ctx, ...) – deduplicates 3 error-path blocks Keep RunContext as a minimal mutable state bag; remove LoopAction enum and the over-extracted response dispatchers (_respond, _handle_tool_calls, _handle_empty, _handle_length, _handle_final, _handle_max_iterations).
This commit is contained in:
chengyongru
parent
d4ade8f680
commit
e977a43445
@ -108,6 +108,49 @@ class AgentRunResult:
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had_injections: bool = False
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@dataclass(slots=True)
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class RunContext:
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"""Mutable accumulator for state that lives across loop iterations."""
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spec: AgentRunSpec
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hook: AgentHook
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messages: list[dict[str, Any]] # OpenAI-format chat messages (mutated in place)
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iteration: int = 0
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# Cached by _prepare_context for downstream retry access.
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# Stale after _prepare_context returns — do not use elsewhere.
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messages_for_model: list[dict[str, Any]] | None = None
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final_content: str | None = None
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tools_used: list[str] = field(default_factory=list)
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usage: dict[str, int] = field(
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default_factory=lambda: {"prompt_tokens": 0, "completion_tokens": 0},
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)
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error: str | None = None
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stop_reason: str = "completed"
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tool_events: list[dict[str, str]] = field(default_factory=list)
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# Per-run throttle counters (keyed by tool name)
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external_lookup_counts: dict[str, int] = field(default_factory=dict)
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workspace_violation_counts: dict[str, int] = field(default_factory=dict)
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empty_content_retries: int = 0
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length_recovery_count: int = 0
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had_injections: bool = False
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injection_cycles: int = 0
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def to_result(self) -> AgentRunResult:
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return AgentRunResult(
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final_content=self.final_content,
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messages=self.messages,
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tools_used=self.tools_used,
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usage=self.usage,
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stop_reason=self.stop_reason,
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error=self.error,
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tool_events=self.tool_events,
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had_injections=self.had_injections,
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)
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class AgentRunner:
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"""Run a tool-capable LLM loop without product-layer concerns."""
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@ -155,47 +198,42 @@ class AgentRunner:
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async def _try_drain_injections(
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self,
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spec: AgentRunSpec,
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messages: list[dict[str, Any]],
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assistant_message: dict[str, Any] | None,
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injection_cycles: int,
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ctx: RunContext,
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*,
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assistant_message: dict[str, Any] | None = None,
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phase: str = "after error",
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iteration: int | None = None,
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) -> tuple[bool, int]:
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"""Drain pending injections. Returns (should_continue, updated_cycles).
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) -> bool:
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"""Drain pending injections into ctx.messages.
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If injections are found and we haven't exceeded _MAX_INJECTION_CYCLES,
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append them to *messages* (and emit a checkpoint if *assistant_message*
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and *iteration* are both provided) and return (True, cycles+1) so the
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caller continues the iteration loop. Otherwise return (False, cycles).
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Returns True if injections were appended (caller should continue).
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Mutates ctx.injection_cycles and ctx.had_injections directly.
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"""
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if injection_cycles >= _MAX_INJECTION_CYCLES:
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return False, injection_cycles
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injections = await self._drain_injections(spec)
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if ctx.injection_cycles >= _MAX_INJECTION_CYCLES:
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return False
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injections = await self._drain_injections(ctx.spec)
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if not injections:
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return False, injection_cycles
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injection_cycles += 1
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return False
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ctx.injection_cycles += 1
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if assistant_message is not None:
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messages.append(assistant_message)
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if iteration is not None:
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await self._emit_checkpoint(
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spec,
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{
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"phase": "final_response",
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"iteration": iteration,
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"model": spec.model,
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"assistant_message": assistant_message,
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"completed_tool_results": [],
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"pending_tool_calls": [],
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},
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)
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self._append_injected_messages(messages, injections)
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ctx.messages.append(assistant_message)
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await self._emit_checkpoint(
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ctx.spec,
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{
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"phase": "final_response",
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"iteration": ctx.iteration,
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"model": ctx.spec.model,
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"assistant_message": assistant_message,
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"completed_tool_results": [],
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"pending_tool_calls": [],
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},
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)
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self._append_injected_messages(ctx.messages, injections)
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ctx.had_injections = True
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logger.info(
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"Injected {} follow-up message(s) {} ({}/{})",
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len(injections), phase, injection_cycles, _MAX_INJECTION_CYCLES,
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len(injections), phase, ctx.injection_cycles, _MAX_INJECTION_CYCLES,
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)
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return True, injection_cycles
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return True
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async def _drain_injections(self, spec: AgentRunSpec) -> list[dict[str, Any]]:
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"""Drain pending user messages via the injection callback.
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@ -243,55 +281,23 @@ class AgentRunner:
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return injected_messages
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async def run(self, spec: AgentRunSpec) -> AgentRunResult:
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hook = spec.hook or AgentHook()
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messages = list(spec.initial_messages)
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final_content: str | None = None
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tools_used: list[str] = []
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usage: dict[str, int] = {"prompt_tokens": 0, "completion_tokens": 0}
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error: str | None = None
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stop_reason = "completed"
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tool_events: list[dict[str, str]] = []
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external_lookup_counts: dict[str, int] = {}
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# Per-turn throttle for repeated attempts against the same outside target.
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workspace_violation_counts: dict[str, int] = {}
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empty_content_retries = 0
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length_recovery_count = 0
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had_injections = False
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injection_cycles = 0
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ctx = RunContext(
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spec=spec,
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hook=spec.hook or AgentHook(),
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messages=list(spec.initial_messages),
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)
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for ctx.iteration in range(spec.max_iterations):
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messages_for_model = await self._prepare_context(ctx)
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hook_context = AgentHookContext(iteration=ctx.iteration, messages=ctx.messages)
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await ctx.hook.before_iteration(hook_context)
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response = await self._request_model(ctx.spec, messages_for_model, ctx.hook, hook_context)
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for iteration in range(spec.max_iterations):
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try:
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# Keep the persisted conversation untouched. Context governance
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# may repair or compact historical messages for the model, but
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# those synthetic edits must not shift the append boundary used
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# later when the caller saves only the new turn.
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messages_for_model = self._drop_orphan_tool_results(messages)
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messages_for_model = self._backfill_missing_tool_results(messages_for_model)
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messages_for_model = self._microcompact(messages_for_model)
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messages_for_model = self._apply_tool_result_budget(spec, messages_for_model)
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messages_for_model = self._snip_history(spec, messages_for_model)
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# Snipping may have created new orphans; clean them up.
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messages_for_model = self._drop_orphan_tool_results(messages_for_model)
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messages_for_model = self._backfill_missing_tool_results(messages_for_model)
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except Exception:
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logger.exception(
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"Context governance failed on turn {} for {}; applying minimal repair",
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iteration,
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spec.session_key or "default",
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)
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try:
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messages_for_model = self._drop_orphan_tool_results(messages)
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messages_for_model = self._backfill_missing_tool_results(messages_for_model)
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except Exception:
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messages_for_model = messages
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context = AgentHookContext(iteration=iteration, messages=messages)
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await hook.before_iteration(context)
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response = await self._request_model(spec, messages_for_model, hook, context)
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raw_usage = self._usage_dict(response.usage)
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context.response = response
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context.usage = dict(raw_usage)
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context.tool_calls = list(response.tool_calls)
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self._accumulate_usage(usage, raw_usage)
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hook_context.response = response
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hook_context.usage = dict(raw_usage)
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hook_context.tool_calls = list(response.tool_calls)
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self._accumulate_usage(ctx.usage, raw_usage)
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reasoning_text, cleaned_content = extract_reasoning(
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response.reasoning_content,
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@ -299,47 +305,49 @@ class AgentRunner:
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response.content,
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)
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response.content = cleaned_content
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if reasoning_text and not context.streamed_reasoning:
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await hook.emit_reasoning(reasoning_text)
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await hook.emit_reasoning_end()
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context.streamed_reasoning = True
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if reasoning_text and not hook_context.streamed_reasoning:
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await ctx.hook.emit_reasoning(reasoning_text)
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await ctx.hook.emit_reasoning_end()
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hook_context.streamed_reasoning = True
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if response.should_execute_tools:
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context.tool_calls = list(response.tool_calls)
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if hook.wants_streaming():
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await hook.on_stream_end(context, resuming=True)
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hook_context.tool_calls = list(response.tool_calls)
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if ctx.hook.wants_streaming():
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await ctx.hook.on_stream_end(hook_context, resuming=True)
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openai_tool_calls = [tc.to_openai_tool_call() for tc in response.tool_calls]
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assistant_message = build_assistant_message(
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response.content or "",
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tool_calls=[tc.to_openai_tool_call() for tc in response.tool_calls],
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tool_calls=openai_tool_calls,
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reasoning_content=response.reasoning_content,
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thinking_blocks=response.thinking_blocks,
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)
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messages.append(assistant_message)
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tools_used.extend(tc.name for tc in response.tool_calls)
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ctx.messages.append(assistant_message)
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ctx.tools_used.extend(tc.name for tc in response.tool_calls)
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await self._emit_checkpoint(
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spec,
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ctx.spec,
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{
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"phase": "awaiting_tools",
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"iteration": iteration,
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"model": spec.model,
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"iteration": ctx.iteration,
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"model": ctx.spec.model,
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"assistant_message": assistant_message,
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"completed_tool_results": [],
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"pending_tool_calls": [tc.to_openai_tool_call() for tc in response.tool_calls],
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"pending_tool_calls": openai_tool_calls,
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},
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)
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await hook.before_execute_tools(context)
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await ctx.hook.before_execute_tools(hook_context)
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results, new_events, fatal_error = await self._execute_tools(
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spec,
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ctx.spec,
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response.tool_calls,
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external_lookup_counts,
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workspace_violation_counts,
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ctx.external_lookup_counts,
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ctx.workspace_violation_counts,
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)
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tool_events.extend(new_events)
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context.tool_results = list(results)
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context.tool_events = list(new_events)
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ctx.tool_events.extend(new_events)
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hook_context.tool_results = list(results)
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hook_context.tool_events = list(new_events)
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completed_tool_results: list[dict[str, Any]] = []
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for tool_call, result in zip(response.tool_calls, results):
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tool_message = {
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@ -347,112 +355,101 @@ class AgentRunner:
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"tool_call_id": tool_call.id,
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"name": tool_call.name,
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"content": self._normalize_tool_result(
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spec,
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ctx.spec,
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tool_call.id,
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tool_call.name,
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result,
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),
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}
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messages.append(tool_message)
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ctx.messages.append(tool_message)
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completed_tool_results.append(tool_message)
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if fatal_error is not None:
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error = f"Error: {type(fatal_error).__name__}: {fatal_error}"
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final_content = error
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stop_reason = "tool_error"
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self._append_final_message(messages, final_content)
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context.final_content = final_content
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context.error = error
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context.stop_reason = stop_reason
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await hook.after_iteration(context)
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should_continue, injection_cycles = await self._try_drain_injections(
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spec, messages, None, injection_cycles,
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if await self._terminate_with_error(
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ctx, hook_context,
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error=f"Error: {type(fatal_error).__name__}: {fatal_error}",
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stop_reason="tool_error",
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append_fn="final",
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phase="after tool error",
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)
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if should_continue:
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had_injections = True
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):
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continue
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break
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await self._emit_checkpoint(
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spec,
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ctx.spec,
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{
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"phase": "tools_completed",
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"iteration": iteration,
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"model": spec.model,
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"iteration": ctx.iteration,
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"model": ctx.spec.model,
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"assistant_message": assistant_message,
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"completed_tool_results": completed_tool_results,
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"pending_tool_calls": [],
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},
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)
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empty_content_retries = 0
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length_recovery_count = 0
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# Checkpoint 1: drain injections after tools, before next LLM call
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_drained, injection_cycles = await self._try_drain_injections(
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spec, messages, None, injection_cycles,
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phase="after tool execution",
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)
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if _drained:
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had_injections = True
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await hook.after_iteration(context)
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ctx.empty_content_retries = 0
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ctx.length_recovery_count = 0
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await self._try_drain_injections(ctx, phase="after tool execution")
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await ctx.hook.after_iteration(hook_context)
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continue
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if response.has_tool_calls:
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logger.warning(
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"Ignoring tool calls under finish_reason='{}' for {}",
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response.finish_reason,
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spec.session_key or "default",
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ctx.spec.session_key or "default",
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)
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clean = hook.finalize_content(context, response.content)
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clean = ctx.hook.finalize_content(hook_context, response.content)
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if response.finish_reason != "error" and is_blank_text(clean):
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empty_content_retries += 1
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if empty_content_retries < _MAX_EMPTY_RETRIES:
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ctx.empty_content_retries += 1
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if ctx.empty_content_retries < _MAX_EMPTY_RETRIES:
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logger.warning(
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"Empty response on turn {} for {} ({}/{}); retrying",
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iteration,
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spec.session_key or "default",
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empty_content_retries,
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ctx.iteration,
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ctx.spec.session_key or "default",
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ctx.empty_content_retries,
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_MAX_EMPTY_RETRIES,
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)
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if hook.wants_streaming():
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await hook.on_stream_end(context, resuming=False)
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await hook.after_iteration(context)
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if ctx.hook.wants_streaming():
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await ctx.hook.on_stream_end(hook_context, resuming=False)
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await ctx.hook.after_iteration(hook_context)
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continue
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logger.warning(
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"Empty response on turn {} for {} after {} retries; attempting finalization",
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iteration,
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spec.session_key or "default",
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empty_content_retries,
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ctx.iteration,
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ctx.spec.session_key or "default",
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ctx.empty_content_retries,
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)
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if hook.wants_streaming():
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await hook.on_stream_end(context, resuming=False)
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response = await self._request_finalization_retry(spec, messages_for_model)
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if ctx.hook.wants_streaming():
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await ctx.hook.on_stream_end(hook_context, resuming=False)
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response = await self._request_finalization_retry(ctx.spec, messages_for_model)
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retry_usage = self._usage_dict(response.usage)
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self._accumulate_usage(usage, retry_usage)
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raw_usage = self._merge_usage(raw_usage, retry_usage)
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context.response = response
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context.usage = dict(raw_usage)
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context.tool_calls = list(response.tool_calls)
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clean = hook.finalize_content(context, response.content)
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self._accumulate_usage(ctx.usage, retry_usage)
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hook_context.response = response
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hook_context.usage = dict(self._merge_usage(hook_context.usage, retry_usage))
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hook_context.tool_calls = list(response.tool_calls)
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clean = ctx.hook.finalize_content(hook_context, response.content)
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if response.finish_reason == "length" and not is_blank_text(clean):
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length_recovery_count += 1
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if length_recovery_count <= _MAX_LENGTH_RECOVERIES:
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ctx.length_recovery_count += 1
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if ctx.length_recovery_count <= _MAX_LENGTH_RECOVERIES:
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logger.info(
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"Output truncated on turn {} for {} ({}/{}); continuing",
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iteration,
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spec.session_key or "default",
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length_recovery_count,
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ctx.iteration,
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ctx.spec.session_key or "default",
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ctx.length_recovery_count,
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_MAX_LENGTH_RECOVERIES,
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)
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if hook.wants_streaming():
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await hook.on_stream_end(context, resuming=True)
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messages.append(build_assistant_message(
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if ctx.hook.wants_streaming():
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await ctx.hook.on_stream_end(hook_context, resuming=True)
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ctx.messages.append(build_assistant_message(
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clean,
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reasoning_content=response.reasoning_content,
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thinking_blocks=response.thinking_blocks,
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))
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messages.append(build_length_recovery_message())
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await hook.after_iteration(context)
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ctx.messages.append(build_length_recovery_message())
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await ctx.hook.after_iteration(hook_context)
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continue
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assistant_message: dict[str, Any] | None = None
|
||||
@ -463,115 +460,131 @@ class AgentRunner:
|
||||
thinking_blocks=response.thinking_blocks,
|
||||
)
|
||||
|
||||
# Check for mid-turn injections BEFORE signaling stream end.
|
||||
# If injections are found we keep the stream alive (resuming=True)
|
||||
# so streaming channels don't prematurely finalize the card.
|
||||
should_continue, injection_cycles = await self._try_drain_injections(
|
||||
spec, messages, assistant_message, injection_cycles,
|
||||
should_continue = await self._try_drain_injections(
|
||||
ctx,
|
||||
assistant_message=assistant_message,
|
||||
phase="after final response",
|
||||
iteration=iteration,
|
||||
)
|
||||
if should_continue:
|
||||
had_injections = True
|
||||
|
||||
if hook.wants_streaming():
|
||||
await hook.on_stream_end(context, resuming=should_continue)
|
||||
if ctx.hook.wants_streaming():
|
||||
await ctx.hook.on_stream_end(hook_context, resuming=should_continue)
|
||||
|
||||
if should_continue:
|
||||
await hook.after_iteration(context)
|
||||
await ctx.hook.after_iteration(hook_context)
|
||||
continue
|
||||
|
||||
if response.finish_reason == "error":
|
||||
final_content = clean or spec.error_message or _DEFAULT_ERROR_MESSAGE
|
||||
stop_reason = "error"
|
||||
error = final_content
|
||||
self._append_model_error_placeholder(messages)
|
||||
context.final_content = final_content
|
||||
context.error = error
|
||||
context.stop_reason = stop_reason
|
||||
await hook.after_iteration(context)
|
||||
should_continue, injection_cycles = await self._try_drain_injections(
|
||||
spec, messages, None, injection_cycles,
|
||||
if await self._terminate_with_error(
|
||||
ctx, hook_context,
|
||||
error=clean or ctx.spec.error_message or _DEFAULT_ERROR_MESSAGE,
|
||||
stop_reason="error",
|
||||
append_fn="model_error",
|
||||
phase="after LLM error",
|
||||
)
|
||||
if should_continue:
|
||||
had_injections = True
|
||||
continue
|
||||
break
|
||||
if is_blank_text(clean):
|
||||
final_content = EMPTY_FINAL_RESPONSE_MESSAGE
|
||||
stop_reason = "empty_final_response"
|
||||
error = final_content
|
||||
self._append_final_message(messages, final_content)
|
||||
context.final_content = final_content
|
||||
context.error = error
|
||||
context.stop_reason = stop_reason
|
||||
await hook.after_iteration(context)
|
||||
should_continue, injection_cycles = await self._try_drain_injections(
|
||||
spec, messages, None, injection_cycles,
|
||||
phase="after empty response",
|
||||
)
|
||||
if should_continue:
|
||||
had_injections = True
|
||||
):
|
||||
continue
|
||||
break
|
||||
|
||||
messages.append(assistant_message or build_assistant_message(
|
||||
if is_blank_text(clean):
|
||||
if await self._terminate_with_error(
|
||||
ctx, hook_context,
|
||||
error=EMPTY_FINAL_RESPONSE_MESSAGE,
|
||||
stop_reason="empty_final_response",
|
||||
append_fn="final",
|
||||
phase="after empty response",
|
||||
):
|
||||
continue
|
||||
break
|
||||
|
||||
ctx.messages.append(assistant_message or build_assistant_message(
|
||||
clean,
|
||||
reasoning_content=response.reasoning_content,
|
||||
thinking_blocks=response.thinking_blocks,
|
||||
))
|
||||
await self._emit_checkpoint(
|
||||
spec,
|
||||
ctx.spec,
|
||||
{
|
||||
"phase": "final_response",
|
||||
"iteration": iteration,
|
||||
"model": spec.model,
|
||||
"assistant_message": messages[-1],
|
||||
"iteration": ctx.iteration,
|
||||
"model": ctx.spec.model,
|
||||
"assistant_message": ctx.messages[-1],
|
||||
"completed_tool_results": [],
|
||||
"pending_tool_calls": [],
|
||||
},
|
||||
)
|
||||
final_content = clean
|
||||
context.final_content = final_content
|
||||
context.stop_reason = stop_reason
|
||||
await hook.after_iteration(context)
|
||||
ctx.final_content = clean
|
||||
hook_context.final_content = ctx.final_content
|
||||
hook_context.stop_reason = ctx.stop_reason
|
||||
await ctx.hook.after_iteration(hook_context)
|
||||
break
|
||||
else:
|
||||
stop_reason = "max_iterations"
|
||||
if spec.max_iterations_message:
|
||||
final_content = spec.max_iterations_message.format(
|
||||
max_iterations=spec.max_iterations,
|
||||
ctx.stop_reason = "max_iterations"
|
||||
if ctx.spec.max_iterations_message:
|
||||
ctx.final_content = ctx.spec.max_iterations_message.format(
|
||||
max_iterations=ctx.spec.max_iterations,
|
||||
)
|
||||
else:
|
||||
final_content = render_template(
|
||||
ctx.final_content = render_template(
|
||||
"agent/max_iterations_message.md",
|
||||
strip=True,
|
||||
max_iterations=spec.max_iterations,
|
||||
max_iterations=ctx.spec.max_iterations,
|
||||
)
|
||||
self._append_final_message(messages, final_content)
|
||||
# Drain any remaining injections so they are appended to the
|
||||
# conversation history instead of being re-published as
|
||||
# independent inbound messages by _dispatch's finally block.
|
||||
# We ignore should_continue here because the for-loop has already
|
||||
# exhausted all iterations.
|
||||
drained_after_max_iterations, injection_cycles = await self._try_drain_injections(
|
||||
spec, messages, None, injection_cycles,
|
||||
phase="after max_iterations",
|
||||
)
|
||||
if drained_after_max_iterations:
|
||||
had_injections = True
|
||||
self._append_final_message(ctx.messages, ctx.final_content)
|
||||
await self._try_drain_injections(ctx, phase="after max_iterations")
|
||||
|
||||
return AgentRunResult(
|
||||
final_content=final_content,
|
||||
messages=messages,
|
||||
tools_used=tools_used,
|
||||
usage=usage,
|
||||
stop_reason=stop_reason,
|
||||
error=error,
|
||||
tool_events=tool_events,
|
||||
had_injections=had_injections,
|
||||
)
|
||||
return ctx.to_result()
|
||||
|
||||
async def _prepare_context(self, ctx: RunContext) -> list[dict[str, Any]]:
|
||||
"""Build a derived message list safe for the LLM provider.
|
||||
|
||||
Applies orphan cleanup, backfill, microcompaction, budget truncation,
|
||||
and history snipping. Never mutates ctx.messages.
|
||||
Stores the result in ctx.messages_for_model for downstream retry access.
|
||||
"""
|
||||
try:
|
||||
msgs = self._drop_orphan_tool_results(ctx.messages)
|
||||
msgs = self._backfill_missing_tool_results(msgs)
|
||||
msgs = self._microcompact(msgs)
|
||||
msgs = self._apply_tool_result_budget(ctx.spec, msgs)
|
||||
msgs = self._snip_history(ctx.spec, msgs)
|
||||
msgs = self._drop_orphan_tool_results(msgs)
|
||||
msgs = self._backfill_missing_tool_results(msgs)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"Context governance failed on turn {} for {}; applying minimal repair",
|
||||
ctx.iteration,
|
||||
ctx.spec.session_key or "default",
|
||||
)
|
||||
try:
|
||||
msgs = self._drop_orphan_tool_results(ctx.messages)
|
||||
msgs = self._backfill_missing_tool_results(msgs)
|
||||
except Exception:
|
||||
msgs = ctx.messages
|
||||
ctx.messages_for_model = msgs
|
||||
return msgs
|
||||
|
||||
async def _terminate_with_error(
|
||||
self,
|
||||
ctx: RunContext,
|
||||
hook_context: AgentHookContext,
|
||||
*,
|
||||
error: str,
|
||||
stop_reason: str,
|
||||
append_fn: str,
|
||||
phase: str,
|
||||
) -> bool:
|
||||
"""Set error state, append a message, and optionally continue via injections."""
|
||||
ctx.final_content = error
|
||||
ctx.error = error
|
||||
ctx.stop_reason = stop_reason
|
||||
if append_fn == "model_error":
|
||||
self._append_model_error_placeholder(ctx.messages)
|
||||
else:
|
||||
self._append_final_message(ctx.messages, error)
|
||||
hook_context.final_content = ctx.final_content
|
||||
hook_context.error = ctx.error
|
||||
hook_context.stop_reason = ctx.stop_reason
|
||||
await ctx.hook.after_iteration(hook_context)
|
||||
return await self._try_drain_injections(ctx, phase=phase)
|
||||
|
||||
def _build_request_kwargs(
|
||||
self,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user