"""Response intake for the conversation turn loop: normalize the raw provider response into the assistant message, splice agent-as-provider projections, fire ``post_api_request``, relay reasoning to the progress callback, and apply the incomplete-scratchpad / Codex-incomplete continuation guards. Nothing here imports ``agent.conversation_loop`` at module level (cycle). """ from __future__ import annotations from dataclasses import dataclass import json import logging import re from typing import Any, Dict, Optional from agent.provider_projection import splice_provider_projection from agent.trajectory import has_incomplete_scratchpad from agent.turn_truncation import continue_codex_incomplete, normalize_response_for_agent, partial_result logger = logging.getLogger("agent.conversation_loop") _REASONING_TAG_RE = re.compile(r'') @dataclass class ResponseIntakeVerdict: """``action``: ``"fallthrough"`` (process ``assistant_message``), ``"continue"`` (retry the iteration: incomplete scratchpad / Codex continuation) or ``"return"`` (``result`` is the turn's result dict). ``assistant_message``/``finish_reason`` are the normalized outputs.""" action: str assistant_message: Any finish_reason: Any result: Optional[Dict[str, Any]] = None def _coerce_content_text(raw: Any) -> str: """Some OpenAI-compatible servers (llama-server) return content as dict/list, which crashes downstream ``.strip()``; normalize to str (multimodal lists → text parts).""" if isinstance(raw, dict): return raw.get("text", "") or raw.get("content", "") or json.dumps(raw) if isinstance(raw, list): parts = [] for part in raw: if isinstance(part, str): parts.append(part) elif isinstance(part, dict) and part.get("type") == "text": parts.append(part.get("text", "")) elif isinstance(part, dict) and "text" in part: parts.append(str(part["text"])) return "\n".join(parts) return str(raw) def _fire_post_api_request_hook( agent: Any, response: Any, assistant_message: Any, finish_reason: Any, *, api_messages: Any, api_call_count: Any, api_duration: Any, api_start_time: Any, api_request_id: Any, effective_task_id: Any, turn_id: Any, ) -> None: from agent.conversation_loop import _moa_reference_metrics_for_hook try: from hermes_cli.lifecycle import has_hook, invoke_hook as _invoke_hook if has_hook("post_api_request"): _invoke_hook( "post_api_request", task_id=effective_task_id, turn_id=turn_id, api_request_id=api_request_id, session_id=agent.session_id or "", platform=agent.platform or "", model=agent.model, provider=agent.provider, base_url=agent.base_url, api_mode=agent.api_mode, api_call_count=api_call_count, api_duration=api_duration, started_at=api_start_time, ended_at=api_start_time + api_duration, # First stream chunk time (epoch s); None if not streamed / no chunk. # TTFB = first_chunk_at - started_at. first_chunk_at=getattr(agent, "_last_api_first_chunk_at", None), finish_reason=finish_reason, message_count=len(api_messages), response_model=getattr(response, "model", None), response=agent._api_response_payload_for_hook( response, assistant_message, finish_reason=finish_reason ), usage=agent._usage_summary_for_api_request_hook(response), assistant_message=assistant_message, assistant_content_chars=len(assistant_message.content or ""), assistant_tool_call_count=len(getattr(assistant_message, "tool_calls", None) or []), moa_references=_moa_reference_metrics_for_hook(agent), ) except Exception: pass def _relay_thinking(agent: Any, content: str) -> None: """Relay the model's text to the progress callback: subagents send the first line to the parent display; any agent with a structured callback gets ``reasoning.available``.""" _think_text = _REASONING_TAG_RE.sub('', content.strip()).strip() first_line = _think_text.split('\n')[0][:80] if _think_text else "" if first_line and getattr(agent, '_delegate_depth', 0) > 0: try: agent.tool_progress_callback("_thinking", first_line) except Exception: pass elif _think_text: try: agent.tool_progress_callback("reasoning.available", "_thinking", _think_text[:500], None) except Exception: pass def normalize_model_response( agent: Any, *, response: Any, messages: Any, api_messages: Any, conversation_history: Any, api_call_count: Any, api_duration: Any, api_start_time: Any, api_request_id: Any, effective_task_id: Any, turn_id: Any, ) -> ResponseIntakeVerdict: """Normalize ``response`` into ``assistant_message`` (str content, never dict/list) and run the post-response hooks and continuation guards, in the original order.""" assistant_message = normalize_response_for_agent(agent, response) finish_reason = assistant_message.finish_reason def _verdict(action: str, result: Optional[Dict[str, Any]] = None) -> ResponseIntakeVerdict: return ResponseIntakeVerdict( action=action, assistant_message=assistant_message, finish_reason=finish_reason, result=result, ) if assistant_message.content is not None and not isinstance(assistant_message.content, str): assistant_message.content = _coerce_content_text(assistant_message.content) # Agent-as-provider projection: splice the provider-agent's own tool work in as # call/result rows before this turn's assistant message; no-op for ordinary providers. splice_provider_projection(agent, response, messages) _fire_post_api_request_hook( agent, response, assistant_message, finish_reason, api_messages=api_messages, api_call_count=api_call_count, api_duration=api_duration, api_start_time=api_start_time, api_request_id=api_request_id, effective_task_id=effective_task_id, turn_id=turn_id, ) content = assistant_message.content if content and not agent.quiet_mode: if agent.verbose_logging: agent._vprint(f"{agent.log_prefix}🤖 Assistant: {content}") else: agent._vprint(f"{agent.log_prefix}🤖 Assistant: {content[:100]}{'...' if len(content) > 100 else ''}") if content and agent.tool_progress_callback: _relay_thinking(agent, content) # Incomplete (opened, never closed): the model ran out of # output tokens mid-reasoning — retry up to 2 times, then save as partial. if has_incomplete_scratchpad(content or ""): agent._incomplete_scratchpad_retries += 1 agent._buffer_vprint("⚠️ Incomplete detected (opened but never closed)") if agent._incomplete_scratchpad_retries <= 2: agent._buffer_vprint(f"🔄 Retrying API call ({agent._incomplete_scratchpad_retries}/2)...") return _verdict("continue") # don't add the broken message agent._flush_status_buffer() agent._vprint(f"{agent.log_prefix}❌ Max retries (2) for incomplete scratchpad. Saving as partial.", force=True) agent._incomplete_scratchpad_retries = 0 rolled_back_messages = agent._get_messages_up_to_last_assistant(messages) agent._cleanup_task_resources(effective_task_id) agent._persist_session(messages, conversation_history) return _verdict("return", partial_result( rolled_back_messages, api_call_count, "Incomplete REASONING_SCRATCHPAD after 2 retries" )) agent._incomplete_scratchpad_retries = 0 if agent.api_mode == "codex_responses" and finish_reason == "incomplete": _codex_result = continue_codex_incomplete( agent, assistant_message, finish_reason, messages=messages, conversation_history=conversation_history, api_call_count=api_call_count, ) if _codex_result is not None: return _verdict("return", _codex_result) return _verdict("continue") if hasattr(agent, "_codex_incomplete_retries"): agent._codex_incomplete_retries = 0 return _verdict("fallthrough")