Teknium
e83816a4d1
For each issue anchor present in BASE 63279301bc non-test .py and absent on HEAD, the BASE comment/docstring block was re-attached at the HEAD location of the code it explained (matched by the distinctive code line / enclosing def). Sentences already covered by an existing HEAD comment were deduped; the issue number always survives. Insert-only: no code lines changed.
400 lines
19 KiB
Python
400 lines
19 KiB
Python
"""Outer-iteration bookkeeping for the conversation turn loop, in call order:
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``begin_iteration`` (pending redirect, interrupt / review-budget / iteration-budget exits),
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``prepare_iteration`` (``agent:step`` callback, skill-nudge counter, pre-API ``/steer`` drain
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into the newest tool result — never a user message —, run-budget wrap-up notice, tool_call
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argument sanitization, interrupt-scaffold ghost-row drop, role-alternation repair),
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``announce_api_call`` (verbose summary / quiet spinner) and, after the retry loop,
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``apply_retry_restarts`` (consumes the ``TurnRetryState`` restart flags). Nothing here
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imports ``agent.conversation_loop`` at module level (cycle)."""
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from __future__ import annotations
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import logging
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import random
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from contextlib import suppress
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from dataclasses import dataclass
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from typing import Any, Dict
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from agent.display import KawaiiSpinner
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from agent.turn_context import reanchor_current_turn_user_idx
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logger = logging.getLogger("agent.conversation_loop")
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@dataclass
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class IterationPrep:
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"""Always ``action == "fallthrough"``. ``messages`` is the (possibly filtered) transcript
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and ``request_logger`` the per-request logger the caller keeps using."""
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action: str
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messages: Any
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request_logger: Any
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def prepare_iteration(agent: Any,*, messages: Any, api_call_count: Any) -> IterationPrep:
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"""Prepare ``messages`` for this iteration in the original order. Every mutation here is
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cache-safe by construction: steer text lands in the newest tool result, the ghost-row
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filter only drops hidden scaffold placeholders, and repair runs BEFORE the request build."""
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from agent.conversation_loop import (
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_INTERRUPT_SCAFFOLD_MARKER, _maybe_inject_run_budget_wrapup
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)
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# Fire step_callback for gateway hooks (agent:step event).
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if agent.step_callback is not None:
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try:
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agent.step_callback(api_call_count, _previous_tool_round(messages))
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except Exception as _step_err:
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logger.debug("step_callback error (iteration %s): %s", api_call_count, _step_err)
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# Tool-calling iterations for the skill nudge; resets whenever skill_manage is used.
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if agent._skill_nudge_interval > 0 and "skill_manage" in agent.valid_tool_names:
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agent._iters_since_skill += 1
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# Drain a /steer sent during the last API call into the newest tool message so
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# it lands THIS iteration. Never put in a user message (breaks alternation).
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_pre_api_steer = agent._drain_pending_steer()
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if _pre_api_steer:
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_inject_steer_into_newest_tool_result(agent, messages, _pre_api_steer)
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# One-shot run-budget wrap-up notice at 80% of agent.run_budget_seconds, via the
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# same cache-safe channel as /steer (newest tool result); off with no budget.
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if getattr(agent, "run_budget_seconds", None):
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_maybe_inject_run_budget_wrapup(agent, messages)
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request_logger = getattr(agent, "logger", None) or logger # same name as the origin module
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# Per-agent validation cursor skips re-parsing tool_call args already validated.
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# Identity-keyed; a rewritten list breaks the prefix match and forces a re-scan.
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_sanitize_cursor = getattr(agent, "_sanitize_args_cursor", None)
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if _sanitize_cursor is None:
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_sanitize_cursor = {}
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with suppress(Exception):
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agent._sanitize_args_cursor = _sanitize_cursor
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repaired_tool_calls = agent._sanitize_tool_call_arguments(
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messages, logger=request_logger, session_id=agent.session_id, cursor=_sanitize_cursor
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)
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if repaired_tool_calls > 0:
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request_logger.info(
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"Sanitized %s corrupted tool_call arguments before request (session=%s)",
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repaired_tool_calls,
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agent.session_id or "-",
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)
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# Drop legacy hidden assistant placeholders carrying the raw interrupt scaffold
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# before repair: replayed, the model echoes/self-replicates.
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def _is_scaffold_ghost(msg: Dict[str, Any]) -> bool:
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return (
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msg.get("display_kind") == "hidden"
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and msg.get("role") == "assistant"
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and any(
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isinstance(msg.get(k), str) and msg[k].strip() == _INTERRUPT_SCAFFOLD_MARKER
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for k in ("content", "api_content")
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)
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)
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messages = [msg for msg in messages if not _is_scaffold_ghost(msg)]
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# Repair malformed role alternation (tool→user / user→user tails): providers
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# return empty content on them and the empty-retry loop spins. The _with_cursor
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# variant also recomputes the SessionDB flush cursor after compaction.
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from agent.agent_runtime_helpers import repair_message_sequence_with_cursor
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repaired_seq = repair_message_sequence_with_cursor(agent, messages)
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if repaired_seq > 0:
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request_logger.info(
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"Repaired %s message-alternation violations before request (session=%s)",
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repaired_seq,
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agent.session_id or "-",
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)
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return IterationPrep(action="fallthrough", messages=messages, request_logger=request_logger)
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def _previous_tool_round(messages: Any) -> list:
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"""The newest assistant tool_calls batch with each call's result, for ``agent:step``."""
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for _idx, _m in enumerate(reversed(messages)):
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if _m.get("role") == "assistant" and _m.get("tool_calls"):
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_results_by_id = {}
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for _tm in messages[len(messages) - _idx:]:
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if _tm.get("role") != "tool":
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break
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_tcid = _tm.get("tool_call_id")
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if _tcid:
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_results_by_id[_tcid] = _tm.get("content", "")
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return [
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{
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"name": tc["function"]["name"],
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"result": _results_by_id.get(tc.get("id")),
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"arguments": tc["function"].get("arguments"),
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}
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for tc in _m["tool_calls"]
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if isinstance(tc, dict)
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]
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return []
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def _inject_steer_into_newest_tool_result(agent: Any, messages: Any, steer_text: str) -> None:
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"""Append the steer marker to the newest tool message; with no tool message, put the
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text back so the post-tool-execution drain delivers it later."""
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for _si in range(len(messages) - 1, -1, -1):
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_sm = messages[_si]
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if isinstance(_sm, dict) and _sm.get("role") == "tool":
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from agent.prompt_builder import format_steer_marker
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marker = format_steer_marker(steer_text)
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existing = _sm.get("content", "")
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if isinstance(existing, str):
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_sm["content"] = existing + marker
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else:
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# Multimodal content blocks — append a text block.
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with suppress(Exception):
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blocks = list(existing) if existing else []
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blocks.append({"type": "text", "text": marker})
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_sm["content"] = blocks
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logger.debug(
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"Pre-API-call steer drain: injected into tool msg at index %d", _si
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)
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return
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_lock = getattr(agent, "_pending_steer_lock", None)
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if _lock is not None:
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with _lock:
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if agent._pending_steer:
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agent._pending_steer = agent._pending_steer + "\n" + steer_text
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else:
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agent._pending_steer = steer_text
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else:
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existing = getattr(agent, "_pending_steer", None)
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agent._pending_steer = (existing + "\n" + steer_text) if existing else steer_text
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@dataclass
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class ApiCallAnnouncement:
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"""Always ``action == "fallthrough"``; ``thinking_spinner`` is the started raw spinner or
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None (TUI widget / streaming consumers / verbose mode)."""
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action: str
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thinking_spinner: Any
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def announce_api_call(
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agent: Any, *, messages: Any, api_messages: Any, api_call_count: Any, approx_tokens: Any,
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total_chars: Any,
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) -> ApiCallAnnouncement:
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"""Print the request summary (verbose) or start the quiet-mode thinking indicator."""
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thinking_spinner = None
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if not agent.quiet_mode:
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agent._vprint(f"\n{agent.log_prefix}🔄 Making API call #{api_call_count}/{agent.max_iterations}...")
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agent._vprint(f"{agent.log_prefix} 📊 Request size: {len(api_messages)} messages, ~{approx_tokens:,} tokens (~{total_chars:,} chars)")
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agent._vprint(f"{agent.log_prefix} 🔧 Available tools: {len(agent.tools) if agent.tools else 0}")
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else:
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# Animated thinking spinner in quiet mode
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face = random.choice(KawaiiSpinner.get_thinking_faces())
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verb = random.choice(KawaiiSpinner.get_thinking_verbs())
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if agent.thinking_callback:
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# CLI TUI mode: use prompt_toolkit widget instead of raw spinner
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# (works in both streaming and non-streaming modes)
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agent.thinking_callback(f"{face} {verb}...")
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elif not agent._has_stream_consumers() and agent._should_start_quiet_spinner():
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# Raw KawaiiSpinner only when no streaming consumers and the
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# spinner output has a safe sink.
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spinner_type = random.choice(['brain', 'sparkle', 'pulse', 'moon', 'star'])
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thinking_spinner = KawaiiSpinner(f"{face} {verb}...", spinner_type=spinner_type, print_fn=agent._print_fn)
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thinking_spinner.start()
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# Log request details if verbose
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if agent.verbose_logging:
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logging.debug(f"API Request - Model: {agent.model}, Messages: {len(messages)}, Tools: {len(agent.tools) if agent.tools else 0}")
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logging.debug(f"Last message role: {messages[-1]['role'] if messages else 'none'}")
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logging.debug(f"Total message size: ~{approx_tokens:,} tokens")
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return ApiCallAnnouncement(action="fallthrough", thinking_spinner=thinking_spinner)
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@dataclass
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class IterationStart:
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"""``action``: ``"fallthrough"`` (run the iteration) or ``"break"`` (turn ends: interrupt,
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review input budget or iteration budget exhausted — ``_turn_exit_reason`` set)."""
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action: str
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original_user_message: Any
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api_call_count: Any
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interrupted: Any
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_turn_exit_reason: Any
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def begin_iteration(
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agent: Any, *, messages: Any, conversation_history: Any, original_user_message: Any,
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api_call_count: Any, interrupted: Any, _turn_exit_reason: Any,
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) -> IterationStart:
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"""Iteration entry in the original order: apply a pending redirect, reset the checkpoint
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dedup, then the interrupt / review-budget / iteration-budget exits. ``api_call_count`` is
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incremented here (the grace call consumes its flag instead of the budget)."""
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from agent.conversation_loop import (
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_apply_active_turn_redirect, _review_input_budget_exhausted
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)
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def _verdict(action: str) -> IterationStart:
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return IterationStart(
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action=action, original_user_message=original_user_message,
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api_call_count=api_call_count, interrupted=interrupted,
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_turn_exit_reason=_turn_exit_reason,
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)
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_redirect_text = agent._drain_pending_redirect()
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if _redirect_text:
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_apply_active_turn_redirect(agent, messages, _redirect_text)
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if isinstance(original_user_message, str):
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original_user_message = (
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f"{original_user_message}\n\n" f"User correction during the turn: {_redirect_text}"
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)
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agent._persist_session(messages, conversation_history)
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# Reset per-turn checkpoint dedup so each iteration can take one snapshot.
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agent._checkpoint_mgr.new_turn()
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if agent._interrupt_requested:
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interrupted = True
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_turn_exit_reason = "interrupted_by_user"
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if not agent.quiet_mode:
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agent._safe_print("\n⚡ Breaking out of tool loop due to interrupt...")
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return _verdict("break")
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# Aggregate input budget for detached auxiliary forks bounds the whole review, not
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# each request; checked between iterations so the crossing request's writes landed.
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if _review_input_budget_exhausted(agent):
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_turn_exit_reason = "review_input_budget_exhausted"
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if not agent.quiet_mode:
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agent._safe_print(
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f"\n⏹️ Review input budget exhausted "
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f"({int(agent.session_input_tokens):,} tokens) — stopping "
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f"the review tool loop before the next provider call."
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)
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return _verdict("break")
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api_call_count += 1
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agent._api_call_count = api_call_count
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agent._touch_activity(f"starting API call #{api_call_count}")
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# Grace call: budget exhausted but the model gets one more call. Consume the
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# flag so the loop exits after this iteration regardless of outcome.
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if agent._budget_grace_call:
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# Iteration budget: the LLM is only notified when it actually exhausts the iteration budget
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# (api_call_count >= max_iterations). At that point we inject ONE message, allow one final API call,
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# and if the model doesn't produce a text response, force a user-message asking it to summarise. No
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# intermediate pressure warnings — they caused models to "give up" prematurely on complex tasks
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# (#7915).
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agent._budget_grace_call = False
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elif not agent.iteration_budget.consume():
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_turn_exit_reason = "budget_exhausted"
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if not agent.quiet_mode:
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agent._safe_print(f"\n⚠️ Iteration budget exhausted ({agent.iteration_budget.used}/{agent.iteration_budget.max_total} iterations used)")
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return _verdict("break")
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return _verdict("fallthrough")
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@dataclass
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class RetryRestartVerdict:
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"""``action``: ``"fallthrough"`` (a response is ready — process it), ``"continue"``
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(a restart flag re-issues the iteration: redirect / compressed / rebuilt-for-fallback /
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length continuation) or ``"break"`` (turn ends: interrupted, non-actionable compaction
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handoff, or every retry exhausted without a response)."""
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action: str
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current_turn_user_idx: Any
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final_response: Any
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retry_count: Any
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api_call_count: Any
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_preflight_compression_blocked: Any
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_turn_exit_reason: Any
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def apply_retry_restarts(
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agent: Any, *, _retry: Any, response: Any, interrupted: Any, messages: Any,
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conversation_history: Any, user_message: Any, api_kwargs: Any, current_turn_user_idx: Any,
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final_response: Any, retry_count: Any, api_call_count: Any, length_continue_retries: Any,
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_preflight_compression_blocked: Any, _turn_exit_reason: Any,
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) -> RetryRestartVerdict:
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"""Consume the ``TurnRetryState`` restart flags after the retry loop, in the original
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priority order. Refunds the iteration budget/count for restarts that produced no valid
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assistant item; ``restart_with_rebuilt_messages`` is the single consumer that clears
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``_preflight_compression_blocked`` so the fallback gets a fresh preflight (#84733)."""
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from agent.conversation_loop import (
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_HANDOFF_SKIP_FINAL_RESPONSE, _should_skip_model_call_for_reference_handoff
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)
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def _verdict(action: str) -> RetryRestartVerdict:
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return RetryRestartVerdict(
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action=action, current_turn_user_idx=current_turn_user_idx,
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final_response=final_response, retry_count=retry_count, api_call_count=api_call_count,
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_preflight_compression_blocked=_preflight_compression_blocked,
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_turn_exit_reason=_turn_exit_reason,
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)
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if _retry.restart_with_redirected_messages:
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# Cancelled request produced no valid assistant item: reuse the same logical
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# iteration after the outer loop appends partial context + correction.
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api_call_count -= 1
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agent.iteration_budget.refund()
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_retry.restart_with_redirected_messages = False
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return _verdict("continue")
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if interrupted:
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_turn_exit_reason = "interrupted_during_api_call"
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return _verdict("break")
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if _retry.restart_with_compressed_messages:
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api_call_count -= 1
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agent.iteration_budget.refund()
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# Compression restarts count toward the retry limit so a compression that
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# shrinks messages but not enough can't loop forever.
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retry_count += 1
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_retry.restart_with_compressed_messages = False
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if _should_skip_model_call_for_reference_handoff(
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# Compression rebuilt the list (tail messages are fresh compaction copies), so the
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# pre-compression index of this turn's user message is stale. Re-anchor both index trackers: the
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# api_content stamp below, the loop's injection site, and the flush's persist-override row
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# (#48677) must all target the surviving dict, not a stale position. Exact-content match first
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# so a todo-snapshot user message appended after the tail can't steal the anchor.
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messages, user_message
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):
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logger.info(
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"Skipping compressed-restart model call: reference-only "
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"handoff would be the sole active user turn (#80622)"
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)
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if not final_response:
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final_response = _HANDOFF_SKIP_FINAL_RESPONSE
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_turn_exit_reason = "compaction_handoff_not_actionable"
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return _verdict("break")
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# In-loop compression rebuilt `messages`; re-anchor the current-turn index
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# like the prologue, AFTER the handoff guard (it may re-append this turn's
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# ask). A stale anchor injects prefetch into a historical row.
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current_turn_user_idx = reanchor_current_turn_user_idx(messages, user_message)
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agent._persist_user_message_idx = current_turn_user_idx
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return _verdict("continue")
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if _retry.restart_with_rebuilt_messages:
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# A stall/failure escalated to the fallback chain: re-issue against the
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# active fallback provider, refunding budget/count for the stalled attempt.
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api_call_count -= 1
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agent.iteration_budget.refund()
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_retry.restart_with_rebuilt_messages = False
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# Failover shrank the compressor window: clear the preflight block so
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# preflight re-runs before the first fallback call (single consumer).
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_preflight_compression_blocked = False
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return _verdict("continue")
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if _retry.restart_with_length_continuation:
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# Boost output budget per retry: 2×, 4×, 8×, 16× base, capped at 32 768, via
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# _ephemeral_max_output_tokens. Keep a larger original provider/model
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# default as the floor so retries never downshift.
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_boost = (agent.max_tokens or 4096) * (2 ** length_continue_retries)
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_requested_cap = agent._requested_output_cap_from_api_kwargs(api_kwargs)
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if _requested_cap is not None:
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_boost = max(_boost, _requested_cap)
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_boost_cap = max(32768, _requested_cap or 0)
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agent._ephemeral_max_output_tokens = min(_boost, _boost_cap)
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return _verdict("continue")
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# All retries may exhaust with `response` still None; break out cleanly.
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if response is None:
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_turn_exit_reason = "all_retries_exhausted_no_response"
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print(f"{agent.log_prefix}❌ All API retries exhausted with no successful response.")
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agent._persist_session(messages, conversation_history)
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return _verdict("break")
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return _verdict("fallthrough")
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