"""Assorted AIAgent runtime helpers — moved out of run_agent.py for clarity. Each function takes the parent ``AIAgent`` as its first argument (``agent``) except for the static helpers (``sanitize_tool_call_arguments``, ``drop_thinking_only_and_merge_users``) which are stateless. AIAgent keeps thin forwarders for backward compatibility. Methods covered: * ``convert_to_trajectory_format`` — internal -> trajectory-file format * ``sanitize_tool_call_arguments`` — repair corrupted JSON in tool_calls * ``repair_message_sequence`` — enforce alternation invariants * ``strip_think_blocks`` — remove inline reasoning from stored content * ``recover_with_credential_pool`` — rotate pool entries on 429 * ``try_recover_primary_transport`` — re-create OpenAI client after rate-limit * ``drop_thinking_only_and_merge_users`` — Anthropic-style cleanup * ``restore_primary_runtime`` — un-do fallback activation * ``extract_reasoning`` — pull reasoning fields out of API responses * ``dump_api_request_debug`` — write request body for post-mortem * ``anthropic_prompt_cache_policy`` — compute cache_control breakpoints * ``create_openai_client`` — build the per-agent OpenAI SDK client """ from __future__ import annotations import copy import json import logging import os import re import threading import time import uuid from datetime import datetime from pathlib import Path from typing import Any, Dict, List, Optional, Tuple from hermes_cli.timeouts import get_provider_request_timeout from agent.message_sanitization import ( _repair_tool_call_arguments, _sanitize_surrogates, ) from agent.tool_dispatch_helpers import _trajectory_normalize_msg, make_tool_result_message from agent.trajectory import convert_scratchpad_to_think from agent.error_classifier import classify_api_error, FailoverReason from utils import base_url_host_matches, base_url_hostname, env_var_enabled, atomic_json_write logger = logging.getLogger(__name__) def _ra(): """Lazy ``run_agent`` reference for test-patch routing.""" import run_agent return run_agent def convert_to_trajectory_format(agent, messages: List[Dict[str, Any]], user_query: str, completed: bool) -> List[Dict[str, Any]]: """ Convert internal message format to trajectory format for saving. Args: messages (List[Dict]): Internal message history user_query (str): Original user query completed (bool): Whether the conversation completed successfully Returns: List[Dict]: Messages in trajectory format """ # Normalize multimodal tool results — trajectories are text-only, so # replace image-bearing tool messages with their text_summary to avoid # embedding ~1MB base64 blobs into every saved trajectory. messages = [_trajectory_normalize_msg(m) for m in messages] trajectory = [] # Add system message with tool definitions system_msg = ( "You are a function calling AI model. You are provided with function signatures within XML tags. " "You may call one or more functions to assist with the user query. If available tools are not relevant in assisting " "with user query, just respond in natural conversational language. Don't make assumptions about what values to plug " "into functions. After calling & executing the functions, you will be provided with function results within " " XML tags. Here are the available tools:\n" f"\n{agent._format_tools_for_system_message()}\n\n" "For each function call return a JSON object, with the following pydantic model json schema for each:\n" "{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, " "'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n" "Each function call should be enclosed within XML tags.\n" "Example:\n\n{'name': ,'arguments': }\n" ) trajectory.append({ "from": "system", "value": system_msg }) # Add the actual user prompt (from the dataset) as the first human message trajectory.append({ "from": "human", "value": user_query }) # Skip the first message (the user query) since we already added it above. # Prefill messages are injected at API-call time only (not in the messages # list), so no offset adjustment is needed here. i = 1 while i < len(messages): msg = messages[i] if msg["role"] == "assistant": # Check if this message has tool calls if "tool_calls" in msg and msg["tool_calls"]: # Format assistant message with tool calls # Add tags around reasoning for trajectory storage content = "" # Prepend reasoning in tags if available (native thinking tokens) if msg.get("reasoning") and msg["reasoning"].strip(): content = f"\n{msg['reasoning']}\n\n" if msg.get("content") and msg["content"].strip(): # Convert any tags to tags # (used when native thinking is disabled and model reasons via XML) content += convert_scratchpad_to_think(msg["content"]) + "\n" # Add tool calls wrapped in XML tags for tool_call in msg["tool_calls"]: if not tool_call or not isinstance(tool_call, dict): continue # Parse arguments - should always succeed since we validate during conversation # but keep try-except as safety net try: arguments = json.loads(tool_call["function"]["arguments"]) if isinstance(tool_call["function"]["arguments"], str) else tool_call["function"]["arguments"] except json.JSONDecodeError: # This shouldn't happen since we validate and retry during conversation, # but if it does, log warning and use empty dict logging.warning(f"Unexpected invalid JSON in trajectory conversion: {tool_call['function']['arguments'][:100]}") arguments = {} tool_call_json = { "name": tool_call["function"]["name"], "arguments": arguments } content += f"\n{json.dumps(tool_call_json, ensure_ascii=False)}\n\n" # Ensure every gpt turn has a block (empty if no reasoning) # so the format is consistent for training data if "" not in content: content = "\n\n" + content trajectory.append({ "from": "gpt", "value": content.rstrip() }) # Collect all subsequent tool responses tool_responses = [] j = i + 1 while j < len(messages) and messages[j]["role"] == "tool": tool_msg = messages[j] # Format tool response with XML tags tool_response = "\n" # Try to parse tool content as JSON if it looks like JSON tool_content = tool_msg["content"] try: if tool_content.strip().startswith(("{", "[")): tool_content = json.loads(tool_content) except (json.JSONDecodeError, AttributeError): pass # Keep as string if not valid JSON tool_index = len(tool_responses) tool_name = ( msg["tool_calls"][tool_index]["function"]["name"] if tool_index < len(msg["tool_calls"]) else "unknown" ) tool_response += json.dumps({ "tool_call_id": tool_msg.get("tool_call_id", ""), "name": tool_name, "content": tool_content }, ensure_ascii=False) tool_response += "\n" tool_responses.append(tool_response) j += 1 # Add all tool responses as a single message if tool_responses: trajectory.append({ "from": "tool", "value": "\n".join(tool_responses) }) i = j - 1 # Skip the tool messages we just processed else: # Regular assistant message without tool calls # Add tags around reasoning for trajectory storage content = "" # Prepend reasoning in tags if available (native thinking tokens) if msg.get("reasoning") and msg["reasoning"].strip(): content = f"\n{msg['reasoning']}\n\n" # Convert any tags to tags # (used when native thinking is disabled and model reasons via XML) raw_content = msg["content"] or "" content += convert_scratchpad_to_think(raw_content) # Ensure every gpt turn has a block (empty if no reasoning) if "" not in content: content = "\n\n" + content trajectory.append({ "from": "gpt", "value": content.strip() }) elif msg["role"] == "user": trajectory.append({ "from": "human", "value": msg["content"] }) i += 1 return trajectory def sanitize_tool_call_arguments( messages: list, *, logger=None, session_id: str = None, ) -> int: """Repair corrupted assistant tool-call argument JSON in-place.""" log = logger or logging.getLogger(__name__) if not isinstance(messages, list): return 0 repaired = 0 marker = _ra().AIAgent._TOOL_CALL_ARGUMENTS_CORRUPTION_MARKER def _prepend_marker(tool_msg: dict) -> None: existing = tool_msg.get("content") if isinstance(existing, str): if not existing: tool_msg["content"] = marker elif not existing.startswith(marker): tool_msg["content"] = f"{marker}\n{existing}" return if existing is None: tool_msg["content"] = marker return try: existing_text = json.dumps(existing) except TypeError: existing_text = str(existing) tool_msg["content"] = f"{marker}\n{existing_text}" message_index = 0 while message_index < len(messages): msg = messages[message_index] if not isinstance(msg, dict) or msg.get("role") != "assistant": message_index += 1 continue tool_calls = msg.get("tool_calls") if not isinstance(tool_calls, list) or not tool_calls: message_index += 1 continue insert_at = message_index + 1 for tool_call in tool_calls: if not isinstance(tool_call, dict): continue function = tool_call.get("function") if not isinstance(function, dict): continue arguments = function.get("arguments") if arguments is None or arguments == "": function["arguments"] = "{}" continue if isinstance(arguments, str) and not arguments.strip(): function["arguments"] = "{}" continue if not isinstance(arguments, str): continue try: json.loads(arguments) except json.JSONDecodeError: tool_call_id = tool_call.get("id") function_name = function.get("name", "?") preview = arguments[:80] log.warning( "Corrupted tool_call arguments repaired before request " "(session=%s, message_index=%s, tool_call_id=%s, function=%s, preview=%r)", session_id or "-", message_index, tool_call_id or "-", function_name, preview, ) function["arguments"] = "{}" existing_tool_msg = None scan_index = message_index + 1 while scan_index < len(messages): candidate = messages[scan_index] if not isinstance(candidate, dict) or candidate.get("role") != "tool": break if candidate.get("tool_call_id") == tool_call_id: existing_tool_msg = candidate break scan_index += 1 if existing_tool_msg is None: messages.insert( insert_at, make_tool_result_message( function_name if function_name != "?" else "", marker, tool_call_id, ), ) insert_at += 1 else: _prepend_marker(existing_tool_msg) repaired += 1 message_index += 1 return repaired def repair_message_sequence(agent, messages: List[Dict]) -> int: """Collapse malformed role-alternation left in the live history. Providers (OpenAI, OpenRouter, Anthropic) expect strict alternation: after the system message, user/tool alternates with assistant, with no two consecutive user messages and no tool-result that doesn't follow an assistant-with-tool_calls. Violations cause silent empty responses on most providers, which triggers the empty-retry loop. This runs right before the API call as a defensive belt — by the time it fires, the scaffolding strip should already have prevented most shapes, but external callers (gateway multi-queue replay, session resume, cron, explicit conversation_history passed in by host code) can feed in already-broken histories. Repairs applied: 1. Stray ``tool`` messages whose ``tool_call_id`` doesn't match any preceding assistant tool_call — dropped. 2. Consecutive ``user`` messages — merged with newline separator so no user input is lost. Deliberately does NOT rewind orphan ``assistant(tool_calls)+tool`` pairs that precede a user message — that pattern IS valid when the previous turn completed normally and the user jumped in to redirect before the model got a continuation turn (the ongoing dialog pattern). The empty-response scaffolding stripper handles the genuinely-broken variant via its flag-gated rewind. Returns the number of repairs made (for logging/telemetry). """ if not messages: return 0 repairs = 0 # Pass 1: drop stray tool messages that don't follow a known # assistant tool_call_id. Uses a rolling set of known ids refreshed # on each assistant message. known_tool_ids: set = set() filtered: List[Dict] = [] for msg in messages: if not isinstance(msg, dict): filtered.append(msg) continue role = msg.get("role") if role == "assistant": known_tool_ids = set() for tc in (msg.get("tool_calls") or []): tc_id = tc.get("id") if isinstance(tc, dict) else None if tc_id: known_tool_ids.add(tc_id) filtered.append(msg) elif role == "tool": tc_id = msg.get("tool_call_id") if tc_id and tc_id in known_tool_ids: filtered.append(msg) else: repairs += 1 else: if role == "user": # A user turn closes the tool-result run; subsequent # tool messages without a fresh assistant tool_call # are orphans. known_tool_ids = set() filtered.append(msg) # Pass 2: merge consecutive user messages. Preserves all user input # so nothing the user typed is lost. merged: List[Dict] = [] for msg in filtered: if ( merged and isinstance(msg, dict) and msg.get("role") == "user" and isinstance(merged[-1], dict) and merged[-1].get("role") == "user" ): prev = merged[-1] prev_content = prev.get("content", "") new_content = msg.get("content", "") # Only merge plain-text content; leave multimodal (list) # content alone — collapsing image/audio blocks risks # mangling the attachment structure. if isinstance(prev_content, str) and isinstance(new_content, str): prev["content"] = ( (prev_content + "\n\n" + new_content) if prev_content and new_content else (prev_content or new_content) ) repairs += 1 continue merged.append(msg) if repairs > 0: # Rewrite in place so downstream paths (persistence, return # value, session DB flush) see the repaired sequence. messages[:] = merged return repairs def strip_think_blocks(agent, content: str) -> str: """Remove reasoning/thinking blocks from content, returning only visible text. Handles four cases: 1. Closed tag pairs (``…``) — the common path when the provider emits complete reasoning blocks. 2. Unterminated open tag at a block boundary (start of text or after a newline) — e.g. MiniMax M2.7 / NIM endpoints where the closing tag is dropped. Everything from the open tag to end of string is stripped. The block-boundary check mirrors ``gateway/stream_consumer.py``'s filter so models that mention ```` in prose aren't over-stripped. 3. Stray orphan open/close tags that slip through. 4. Tag variants: ````, ````, ````, ````, ```` (Gemma 4), all case-insensitive. Additionally strips standalone tool-call XML blocks that some open models (notably Gemma variants on OpenRouter) emit inside assistant content instead of via the structured ``tool_calls`` field: * ``…`` * ``…`` * ``…`` * ``…`` * ``…`` * ``…`` (Gemma style) Ported from openclaw/openclaw#67318. The ```` variant is boundary-gated (only strips when the tag sits at start-of-line or after punctuation and carries a ``name="..."`` attribute) so prose mentions like "Use in JavaScript" are preserved. """ if not content: return "" # 1. Closed tag pairs — case-insensitive for all variants so # mixed-case tags (, ) don't slip through to # the unterminated-tag pass and take trailing content with them. content = re.sub(r'.*?', '', content, flags=re.DOTALL | re.IGNORECASE) content = re.sub(r'.*?', '', content, flags=re.DOTALL | re.IGNORECASE) content = re.sub(r'.*?', '', content, flags=re.DOTALL | re.IGNORECASE) content = re.sub(r'.*?', '', content, flags=re.DOTALL | re.IGNORECASE) content = re.sub(r'.*?', '', content, flags=re.DOTALL | re.IGNORECASE) # 1b. Tool-call XML blocks (openclaw/openclaw#67318). Handle the # generic tag names first — they have no attribute gating since # a literal in prose is already vanishingly rare. for _tc_name in ("tool_call", "tool_calls", "tool_result", "function_call", "function_calls"): content = re.sub( rf'<{_tc_name}\b[^>]*>.*?', '', content, flags=re.DOTALL | re.IGNORECASE, ) # 1c. ... — Gemma-style standalone # tool call. Only strip when the tag sits at a block boundary # (start of text, after a newline, or after sentence-ending # punctuation) AND carries a name="..." attribute. This keeps # prose mentions like "Use to declare" safe. content = re.sub( r'(?:(?<=^)|(?<=[\n\r.!?:]))[ \t]*' r']*\bname\s*=[^>]*>' r'(?:(?:(?!).)*)', '', content, flags=re.DOTALL | re.IGNORECASE, ) # 2. Unterminated reasoning block — open tag at a block boundary # (start of text, or after a newline) with no matching close. # Strip from the tag to end of string. Fixes #8878 / #9568 # (MiniMax M2.7 leaking raw reasoning into assistant content). content = re.sub( r'(?:^|\n)[ \t]*<(?:think|thinking|reasoning|thought|REASONING_SCRATCHPAD)\b[^>]*>.*$', '', content, flags=re.DOTALL | re.IGNORECASE, ) # 3. Stray orphan open/close tags that slipped through. content = re.sub( r'\s*', '', content, flags=re.IGNORECASE, ) # 3b. Stray tool-call closers. (We do NOT strip bare or # unterminated because a truncated tail # during streaming may still be valuable to the user; matches # OpenClaw's intentional asymmetry.) content = re.sub( r'\s*', '', content, flags=re.IGNORECASE, ) return content def recover_with_credential_pool( agent, *, status_code: Optional[int], has_retried_429: bool, classified_reason: Optional[FailoverReason] = None, error_context: Optional[Dict[str, Any]] = None, ) -> tuple[bool, bool]: """Attempt credential recovery via pool rotation. Returns (recovered, has_retried_429). On rate limits: first occurrence retries same credential (sets flag True). second consecutive failure rotates to next credential. On billing exhaustion: immediately rotates. On auth failures: attempts token refresh before rotating. `classified_reason` lets the recovery path honor the structured error classifier instead of relying only on raw HTTP codes. This matters for providers that surface billing/rate-limit/auth conditions under a different status code, such as Anthropic returning HTTP 400 for "out of extra usage". """ pool = agent._credential_pool if pool is None: return False, has_retried_429 effective_reason = classified_reason if effective_reason is None: if status_code == 402: effective_reason = FailoverReason.billing elif status_code == 429: effective_reason = FailoverReason.rate_limit elif status_code in {401, 403}: effective_reason = FailoverReason.auth if effective_reason == FailoverReason.billing: rotate_status = status_code if status_code is not None else 402 next_entry = pool.mark_exhausted_and_rotate(status_code=rotate_status, error_context=error_context) if next_entry is not None: _ra().logger.info( "Credential %s (billing) — rotated to pool entry %s", rotate_status, getattr(next_entry, "id", "?"), ) agent._swap_credential(next_entry) return True, False return False, has_retried_429 if effective_reason == FailoverReason.rate_limit: usage_limit_reached = False if error_context: context_reason = str(error_context.get("reason") or "").lower() context_message = str(error_context.get("message") or "").lower() usage_limit_reached = ( "usage_limit_reached" in context_reason or "usage limit has been reached" in context_message ) if not has_retried_429 and not usage_limit_reached: return False, True rotate_status = status_code if status_code is not None else 429 next_entry = pool.mark_exhausted_and_rotate(status_code=rotate_status, error_context=error_context) if next_entry is not None: _ra().logger.info( "Credential %s (rate limit) — rotated to pool entry %s", rotate_status, getattr(next_entry, "id", "?"), ) agent._swap_credential(next_entry) return True, False return False, True if effective_reason == FailoverReason.auth: # Subscription/entitlement 403s look like auth failures on the wire # but refresh cannot fix them — the OAuth token is already valid, # the account simply lacks the entitlement. Without this guard, # ``try_refresh_current()`` keeps minting fresh tokens against the # same unsubscribed account and the main agent loop spins re-issuing # the same 403 until the user Ctrl+C's. # # Defense-in-depth for #26847: xAI's backend has been seen to 403 # standard SuperGrok subscribers with bodies that don't match the # existing entitlement keyword set in ``_is_entitlement_failure``. # Any 403 against ``xai-oauth`` is treated as entitlement here so # the refresh loop can't spin in those cases either. is_entitlement = agent._is_entitlement_failure(error_context, status_code) if not is_entitlement and status_code == 403 and (agent.provider or "") == "xai-oauth": is_entitlement = True if is_entitlement: _ra().logger.info( "Credential %s — entitlement-shaped 403 from %s; " "skipping pool refresh (account lacks subscription, " "not a transient auth failure).", status_code if status_code is not None else "auth", agent.provider or "provider", ) return False, has_retried_429 refreshed = pool.try_refresh_current() if refreshed is not None: _ra().logger.info(f"Credential auth failure — refreshed pool entry {getattr(refreshed, 'id', '?')}") agent._swap_credential(refreshed) return True, has_retried_429 # Refresh failed — rotate to next credential instead of giving up. # The failed entry is already marked exhausted by try_refresh_current(). rotate_status = status_code if status_code is not None else 401 next_entry = pool.mark_exhausted_and_rotate(status_code=rotate_status, error_context=error_context) if next_entry is not None: _ra().logger.info( "Credential %s (auth refresh failed) — rotated to pool entry %s", rotate_status, getattr(next_entry, "id", "?"), ) agent._swap_credential(next_entry) return True, False return False, has_retried_429 def try_recover_primary_transport( agent, api_error: Exception, *, retry_count: int, max_retries: int, ) -> bool: """Attempt one extra primary-provider recovery cycle for transient transport failures. After ``max_retries`` exhaust, rebuild the primary client (clearing stale connection pools) and give it one more attempt before falling back. This is most useful for direct endpoints (custom, Z.AI, Anthropic, OpenAI, local models) where a TCP-level hiccup does not mean the provider is down. Skipped for proxy/aggregator providers (OpenRouter, Nous) which already manage connection pools and retries server-side — if our retries through them are exhausted, one more rebuilt client won't help. """ if agent._fallback_activated: return False # Only for transient transport errors error_type = type(api_error).__name__ if error_type not in _TRANSIENT_TRANSPORT_ERRORS: return False # Skip for aggregator providers — they manage their own retry infra if agent._is_openrouter_url(): return False provider_lower = (agent.provider or "").strip().lower() if provider_lower in {"nous", "nous-research"}: return False try: # Close existing client to release stale connections if getattr(agent, "client", None) is not None: try: agent._close_openai_client( agent.client, reason="primary_recovery", shared=True, ) except Exception: pass # Rebuild from primary snapshot rt = agent._primary_runtime agent._client_kwargs = dict(rt["client_kwargs"]) agent.model = rt["model"] agent.provider = rt["provider"] agent.base_url = rt["base_url"] agent.api_mode = rt["api_mode"] if hasattr(agent, "_transport_cache"): agent._transport_cache.clear() agent.api_key = rt["api_key"] if agent.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_client agent._anthropic_api_key = rt["anthropic_api_key"] agent._anthropic_base_url = rt["anthropic_base_url"] agent._anthropic_client = build_anthropic_client( rt["anthropic_api_key"], rt["anthropic_base_url"], timeout=get_provider_request_timeout(agent.provider, agent.model), ) agent._is_anthropic_oauth = rt["is_anthropic_oauth"] agent.client = None else: agent.client = agent._create_openai_client( dict(rt["client_kwargs"]), reason="primary_recovery", shared=True, ) wait_time = min(3 + retry_count, 8) agent._vprint( f"{agent.log_prefix}🔁 Transient {error_type} on {agent.provider} — " f"rebuilt client, waiting {wait_time}s before one last primary attempt.", force=True, ) time.sleep(wait_time) return True except Exception as e: logging.warning("Primary transport recovery failed: %s", e) return False # ── End provider fallback ────────────────────────────────────────────── def drop_thinking_only_and_merge_users( messages: List[Dict[str, Any]], ) -> List[Dict[str, Any]]: """Drop thinking-only assistant turns; merge any adjacent user messages left behind. Runs on the per-call ``api_messages`` copy only. The stored conversation history (``agent.messages``) is never mutated, so the user still sees the thinking block in the CLI/gateway transcript and session persistence keeps the full trace. Only the wire copy sent to the provider is cleaned. Why drop-and-merge rather than inject stub text: - Fabricating ``"."`` / ``"(continued)"`` text lies in the history and makes future turns see model output the model didn't emit. - Dropping the turn preserves honesty; merging adjacent user messages preserves the provider's role-alternation invariant. - This is the pattern used by Claude Code's ``normalizeMessagesForAPI`` (filterOrphanedThinkingOnlyMessages + mergeAdjacentUserMessages). """ if not messages: return messages # Pass 1: drop thinking-only assistant turns. kept = [m for m in messages if not _ra().AIAgent._is_thinking_only_assistant(m)] dropped = len(messages) - len(kept) if dropped == 0: return messages # Pass 2: merge any newly-adjacent user messages. merged: List[Dict[str, Any]] = [] merges = 0 for m in kept: prev = merged[-1] if merged else None if ( prev is not None and prev.get("role") == "user" and m.get("role") == "user" ): prev_content = prev.get("content", "") cur_content = m.get("content", "") # Work on a copy of ``prev`` so the caller's input dicts are # never mutated. ``_sanitize_api_messages`` upstream already # hands us per-call copies, but staying pure here means we # can be called safely from anywhere (tests, other loops). prev_copy = dict(prev) # Only string-content merge is meaningful for role-alternation # purposes. If either side is a list (multimodal), append as a # separate block rather than collapsing. if isinstance(prev_content, str) and isinstance(cur_content, str): sep = "\n\n" if prev_content and cur_content else "" prev_copy["content"] = prev_content + sep + cur_content elif isinstance(prev_content, list) and isinstance(cur_content, list): prev_copy["content"] = list(prev_content) + list(cur_content) elif isinstance(prev_content, list) and isinstance(cur_content, str): if cur_content: prev_copy["content"] = list(prev_content) + [ {"type": "text", "text": cur_content} ] else: prev_copy["content"] = list(prev_content) elif isinstance(prev_content, str) and isinstance(cur_content, list): new_blocks: List[Dict[str, Any]] = [] if prev_content: new_blocks.append({"type": "text", "text": prev_content}) new_blocks.extend(cur_content) prev_copy["content"] = new_blocks else: # Unknown content shape — fall back to appending separately # (violates alternation, but safer than raising in a hot path). merged.append(m) continue merged[-1] = prev_copy merges += 1 else: merged.append(m) _ra().logger.debug( "Pre-call sanitizer: dropped %d thinking-only assistant turn(s), " "merged %d adjacent user message(s)", dropped, merges, ) return merged def restore_primary_runtime(agent) -> bool: """Restore the primary runtime at the start of a new turn. In long-lived CLI sessions a single AIAgent instance spans multiple turns. Without restoration, one transient failure pins the session to the fallback provider for every subsequent turn. Calling this at the top of ``run_conversation()`` makes fallback turn-scoped. The gateway caches agents across messages (``_agent_cache`` in ``gateway/run.py``), so this restoration IS needed there too. """ if not agent._fallback_activated: # Reset the chain index even when no fallback was activated this # turn. Without this, a turn where _try_activate_fallback() was # called but returned False (chain exhausted or provider not # configured) leaves _fallback_index >= len(_fallback_chain) while # _fallback_activated stays False. The next turn skips this block # entirely, stranding the index and silently blocking all future # fallback attempts for the session. Fixes #20465. agent._fallback_index = 0 return False if getattr(agent, "_rate_limited_until", 0) > time.monotonic(): return False # primary still in rate-limit cooldown, stay on fallback rt = agent._primary_runtime try: # ── Core runtime state ── agent.model = rt["model"] agent.provider = rt["provider"] agent.base_url = rt["base_url"] # setter updates _base_url_lower agent.api_mode = rt["api_mode"] if hasattr(agent, "_transport_cache"): agent._transport_cache.clear() agent.api_key = rt["api_key"] agent._client_kwargs = dict(rt["client_kwargs"]) agent._use_prompt_caching = rt["use_prompt_caching"] # Default to native layout when the restored snapshot predates the # native-vs-proxy split (older sessions saved before this PR). agent._use_native_cache_layout = rt.get( "use_native_cache_layout", agent.api_mode == "anthropic_messages" and agent.provider == "anthropic", ) # ── Rebuild client for the primary provider ── if agent.api_mode == "anthropic_messages": from agent.anthropic_adapter import build_anthropic_client agent._anthropic_api_key = rt["anthropic_api_key"] agent._anthropic_base_url = rt["anthropic_base_url"] agent._anthropic_client = build_anthropic_client( rt["anthropic_api_key"], rt["anthropic_base_url"], timeout=get_provider_request_timeout(agent.provider, agent.model), ) agent._is_anthropic_oauth = rt["is_anthropic_oauth"] agent.client = None else: agent.client = agent._create_openai_client( dict(rt["client_kwargs"]), reason="restore_primary", shared=True, ) # ── Restore context engine state ── cc = agent.context_compressor cc.update_model( model=rt["compressor_model"], context_length=rt["compressor_context_length"], base_url=rt["compressor_base_url"], api_key=rt["compressor_api_key"], provider=rt["compressor_provider"], ) # ── Reset fallback chain for the new turn ── agent._fallback_activated = False agent._fallback_index = 0 logging.info( "Primary runtime restored for new turn: %s (%s)", agent.model, agent.provider, ) return True except Exception as e: logging.warning("Failed to restore primary runtime: %s", e) return False # Which error types indicate a transient transport failure worth # one more attempt with a rebuilt client / connection pool. _TRANSIENT_TRANSPORT_ERRORS = frozenset({ "ReadTimeout", "ConnectTimeout", "PoolTimeout", "ConnectError", "RemoteProtocolError", "APIConnectionError", "APITimeoutError", }) def extract_reasoning(agent, assistant_message) -> Optional[str]: """ Extract reasoning/thinking content from an assistant message. OpenRouter and various providers can return reasoning in multiple formats: 1. message.reasoning - Direct reasoning field (DeepSeek, Qwen, etc.) 2. message.reasoning_content - Alternative field (Moonshot AI, Novita, etc.) 3. message.reasoning_details - Array of {type, summary, ...} objects (OpenRouter unified) Args: assistant_message: The assistant message object from the API response Returns: Combined reasoning text, or None if no reasoning found """ reasoning_parts = [] # Check direct reasoning field if hasattr(assistant_message, 'reasoning') and assistant_message.reasoning: reasoning_parts.append(assistant_message.reasoning) # Check reasoning_content field (alternative name used by some providers) if hasattr(assistant_message, 'reasoning_content') and assistant_message.reasoning_content: # Don't duplicate if same as reasoning if assistant_message.reasoning_content not in reasoning_parts: reasoning_parts.append(assistant_message.reasoning_content) # Check reasoning_details array (OpenRouter unified format) # Format: [{"type": "reasoning.summary", "summary": "...", ...}, ...] if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details: for detail in assistant_message.reasoning_details: if isinstance(detail, dict): # Extract summary from reasoning detail object summary = ( detail.get('summary') or detail.get('thinking') or detail.get('content') or detail.get('text') ) if summary and summary not in reasoning_parts: reasoning_parts.append(summary) # Some providers embed reasoning directly inside assistant content # instead of returning structured reasoning fields. Only fall back # to inline extraction when no structured reasoning was found. content = getattr(assistant_message, "content", None) if not reasoning_parts and isinstance(content, list): # DeepSeek V4 Pro (and compatible providers) return content as a # list of typed blocks, e.g.: # [{"type": "thinking", "thinking": "..."}, {"type": "output", ...}] # Without this branch the thinking text is silently dropped and the # next turn fails with HTTP 400 ("thinking must be passed back"). # Refs #21944. for block in content: if isinstance(block, dict) and block.get("type") == "thinking": thinking_text = block.get("thinking") or block.get("text") or "" thinking_text = thinking_text.strip() if thinking_text and thinking_text not in reasoning_parts: reasoning_parts.append(thinking_text) if not reasoning_parts and isinstance(content, str) and content: inline_patterns = ( r"(.*?)", r"(.*?)", r"(.*?)", r"(.*?)", r"(.*?)", ) for pattern in inline_patterns: flags = re.DOTALL | re.IGNORECASE for block in re.findall(pattern, content, flags=flags): cleaned = block.strip() if cleaned and cleaned not in reasoning_parts: reasoning_parts.append(cleaned) # Combine all reasoning parts if reasoning_parts: return "\n\n".join(reasoning_parts) return None def dump_api_request_debug( agent, api_kwargs: Dict[str, Any], *, reason: str, error: Optional[Exception] = None, ) -> Optional[Path]: """ Dump a debug-friendly HTTP request record for the active inference API. Captures the request body from api_kwargs (excluding transport-only keys like timeout). Intended for debugging provider-side 4xx failures where retries are not useful. """ try: body = copy.deepcopy(api_kwargs) body.pop("timeout", None) body = {k: v for k, v in body.items() if v is not None} api_key = None try: api_key = getattr(agent.client, "api_key", None) except Exception as e: _ra().logger.debug("Could not extract API key for debug dump: %s", e) dump_payload: Dict[str, Any] = { "timestamp": datetime.now().isoformat(), "session_id": agent.session_id, "reason": reason, "request": { "method": "POST", "url": f"{agent.base_url.rstrip('/')}{'/responses' if agent.api_mode == 'codex_responses' else '/chat/completions'}", "headers": { "Authorization": f"Bearer {agent._mask_api_key_for_logs(api_key)}", "Content-Type": "application/json", }, "body": body, }, } if error is not None: error_info: Dict[str, Any] = { "type": type(error).__name__, "message": str(error), } for attr_name in ("status_code", "request_id", "code", "param", "type"): attr_value = getattr(error, attr_name, None) if attr_value is not None: error_info[attr_name] = attr_value body_attr = getattr(error, "body", None) if body_attr is not None: error_info["body"] = body_attr response_obj = getattr(error, "response", None) if response_obj is not None: try: error_info["response_status"] = getattr(response_obj, "status_code", None) error_info["response_text"] = response_obj.text except Exception as e: _ra().logger.debug("Could not extract error response details: %s", e) dump_payload["error"] = error_info timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f") dump_file = agent.logs_dir / f"request_dump_{agent.session_id}_{timestamp}.json" dump_file.write_text( json.dumps(dump_payload, ensure_ascii=False, indent=2, default=str), encoding="utf-8", ) agent._vprint(f"{agent.log_prefix}🧾 Request debug dump written to: {dump_file}") if env_var_enabled("HERMES_DUMP_REQUEST_STDOUT"): print(json.dumps(dump_payload, ensure_ascii=False, indent=2, default=str)) return dump_file except Exception as dump_error: if agent.verbose_logging: logging.warning(f"Failed to dump API request debug payload: {dump_error}") return None def anthropic_prompt_cache_policy( agent, *, provider: Optional[str] = None, base_url: Optional[str] = None, api_mode: Optional[str] = None, model: Optional[str] = None, ) -> tuple[bool, bool]: """Decide whether to apply Anthropic prompt caching and which layout to use. Returns ``(should_cache, use_native_layout)``: * ``should_cache`` — inject ``cache_control`` breakpoints for this request (applies to OpenRouter Claude, native Anthropic, and third-party gateways that speak the native Anthropic protocol). * ``use_native_layout`` — place markers on the *inner* content blocks (native Anthropic accepts and requires this layout); when False markers go on the message envelope (OpenRouter and OpenAI-wire proxies expect the looser layout). Third-party providers using the native Anthropic transport (``api_mode == 'anthropic_messages'`` + Claude-named model) get caching with the native layout so they benefit from the same cost reduction as direct Anthropic callers, provided their gateway implements the Anthropic cache_control contract (MiniMax, Zhipu GLM, LiteLLM's Anthropic proxy mode all do). Qwen / Alibaba-family models on OpenCode, OpenCode Go, and direct Alibaba (DashScope) also honour Anthropic-style ``cache_control`` markers on OpenAI-wire chat completions. Upstream pi-mono #3392 / pi #3393 documented this for opencode-go Qwen. Without markers these providers serve zero cache hits, re-billing the full prompt on every turn. """ eff_provider = (provider if provider is not None else agent.provider) or "" eff_base_url = base_url if base_url is not None else (agent.base_url or "") eff_api_mode = api_mode if api_mode is not None else (agent.api_mode or "") eff_model = (model if model is not None else agent.model) or "" model_lower = eff_model.lower() provider_lower = eff_provider.lower() is_claude = "claude" in model_lower is_openrouter = base_url_host_matches(eff_base_url, "openrouter.ai") # Nous Portal proxies to OpenRouter behind the scenes — identical # OpenAI-wire envelope cache_control semantics. Treat it as an # OpenRouter-equivalent endpoint for caching layout purposes. is_nous_portal = "nousresearch" in eff_base_url.lower() is_anthropic_wire = eff_api_mode == "anthropic_messages" is_native_anthropic = ( is_anthropic_wire and (eff_provider == "anthropic" or base_url_hostname(eff_base_url) == "api.anthropic.com") ) if is_native_anthropic: return True, True if (is_openrouter or is_nous_portal) and is_claude: return True, False # Nous Portal Qwen (e.g. qwen3.6-plus) takes the same envelope-layout # cache_control path as Portal Claude. Portal proxies to OpenRouter # and the upstream Qwen route accepts cache_control markers; without # this branch the alibaba-family check below only matches # provider=opencode/alibaba and Portal traffic falls through to # (False, False), serving 0% cache hits and re-billing the full # prompt on every turn. if is_nous_portal and "qwen" in model_lower: return True, False if is_anthropic_wire and is_claude: # Third-party Anthropic-compatible gateway. return True, True # MiniMax on its Anthropic-compatible endpoint serves its own # model family (MiniMax-M2.7, M2.5, M2.1, M2) with documented # cache_control support (0.1× read pricing, 5-minute TTL). The # blanket is_claude gate above excludes these — opt them in # explicitly via provider id or host match so users on # provider=minimax / minimax-cn (or custom endpoints pointing at # api.minimax.io/anthropic / api.minimaxi.com/anthropic) get the # same cost reduction as Claude traffic. # Docs: https://platform.minimax.io/docs/api-reference/anthropic-api-compatible-cache if is_anthropic_wire: is_minimax_provider = provider_lower in {"minimax", "minimax-cn"} is_minimax_host = ( base_url_host_matches(eff_base_url, "api.minimax.io") or base_url_host_matches(eff_base_url, "api.minimaxi.com") ) if is_minimax_provider or is_minimax_host: return True, True # Qwen/Alibaba on OpenCode (Zen/Go) and native DashScope: OpenAI-wire # transport that accepts Anthropic-style cache_control markers and # rewards them with real cache hits. Without this branch # qwen3.6-plus on opencode-go reports 0% cached tokens and burns # through the subscription on every turn. model_is_qwen = "qwen" in model_lower provider_is_alibaba_family = provider_lower in { "opencode", "opencode-zen", "opencode-go", "alibaba", } if provider_is_alibaba_family and model_is_qwen: # Envelope layout (native_anthropic=False): markers on inner # content parts, not top-level tool messages. Matches # pi-mono's "alibaba" cacheControlFormat. return True, False return False, False def create_openai_client(agent, client_kwargs: dict, *, reason: str, shared: bool) -> Any: from agent.auxiliary_client import _validate_base_url, _validate_proxy_env_urls # Treat client_kwargs as read-only. Callers pass agent._client_kwargs (or shallow # copies of it) in; any in-place mutation leaks back into the stored dict and is # reused on subsequent requests. #10933 hit this by injecting an httpx.Client # transport that was torn down after the first request, so the next request # wrapped a closed transport and raised "Cannot send a request, as the client # has been closed" on every retry. The revert resolved that specific path; this # copy locks the contract so future transport/keepalive work can't reintroduce # the same class of bug. client_kwargs = dict(client_kwargs) _validate_proxy_env_urls() _validate_base_url(client_kwargs.get("base_url")) if agent.provider == "copilot-acp" or str(client_kwargs.get("base_url", "")).startswith("acp://copilot"): from agent.copilot_acp_client import CopilotACPClient client = CopilotACPClient(**client_kwargs) _ra().logger.info( "Copilot ACP client created (%s, shared=%s) %s", reason, shared, agent._client_log_context(), ) return client if agent.provider == "google-gemini-cli" or str(client_kwargs.get("base_url", "")).startswith("cloudcode-pa://"): from agent.gemini_cloudcode_adapter import GeminiCloudCodeClient # Strip OpenAI-specific kwargs the Gemini client doesn't accept safe_kwargs = { k: v for k, v in client_kwargs.items() if k in {"api_key", "base_url", "default_headers", "project_id", "timeout"} } client = GeminiCloudCodeClient(**safe_kwargs) _ra().logger.info( "Gemini Cloud Code Assist client created (%s, shared=%s) %s", reason, shared, agent._client_log_context(), ) return client if agent.provider == "gemini": from agent.gemini_native_adapter import GeminiNativeClient, is_native_gemini_base_url base_url = str(client_kwargs.get("base_url", "") or "") if is_native_gemini_base_url(base_url): safe_kwargs = { k: v for k, v in client_kwargs.items() if k in {"api_key", "base_url", "default_headers", "timeout", "http_client"} } if "http_client" not in safe_kwargs: keepalive_http = agent._build_keepalive_http_client(base_url) if keepalive_http is not None: safe_kwargs["http_client"] = keepalive_http client = GeminiNativeClient(**safe_kwargs) _ra().logger.info( "Gemini native client created (%s, shared=%s) %s", reason, shared, agent._client_log_context(), ) return client # Inject TCP keepalives so the kernel detects dead provider connections # instead of letting them sit silently in CLOSE-WAIT (#10324). Without # this, a peer that drops mid-stream leaves the socket in a state where # epoll_wait never fires, ``httpx`` read timeout may not trigger, and # the agent hangs until manually killed. Probes after 30s idle, retry # every 10s, give up after 3 → dead peer detected within ~60s. # # Safety against #10933: the ``client_kwargs = dict(client_kwargs)`` # above means this injection only lands in the local per-call copy, # never back into ``agent._client_kwargs``. Each ``_create_openai_client`` # invocation therefore gets its OWN fresh ``httpx.Client`` whose # lifetime is tied to the OpenAI client it is passed to. When the # OpenAI client is closed (rebuild, teardown, credential rotation), # the paired ``httpx.Client`` closes with it, and the next call # constructs a fresh one — no stale closed transport can be reused. # Tests in ``tests/run_agent/test_create_openai_client_reuse.py`` and # ``tests/run_agent/test_sequential_chats_live.py`` pin this invariant. if "http_client" not in client_kwargs: keepalive_http = agent._build_keepalive_http_client(client_kwargs.get("base_url", "")) if keepalive_http is not None: client_kwargs["http_client"] = keepalive_http # Uses the module-level `OpenAI` name, resolved lazily on first # access via __getattr__ below. Tests patch via `run_agent.OpenAI`. client = _ra().OpenAI(**client_kwargs) _ra().logger.info( "OpenAI client created (%s, shared=%s) %s", reason, shared, agent._client_log_context(), ) return client def switch_model(agent, new_model, new_provider, api_key='', base_url='', api_mode=''): """Switch the model/provider in-place for a live agent. Called by the /model command handlers (CLI and gateway) after ``model_switch.switch_model()`` has resolved credentials and validated the model. This method performs the actual runtime swap: rebuilding clients, updating caching flags, and refreshing the context compressor. The implementation mirrors ``_try_activate_fallback()`` for the client-swap logic but also updates ``_primary_runtime`` so the change persists across turns (unlike fallback which is turn-scoped). """ from hermes_cli.providers import determine_api_mode # ── Determine api_mode if not provided ── if not api_mode: api_mode = determine_api_mode(new_provider, base_url) # Defense-in-depth: ensure OpenCode base_url doesn't carry a trailing # /v1 into the anthropic_messages client, which would cause the SDK to # hit /v1/v1/messages. `model_switch.switch_model()` already strips # this, but we guard here so any direct callers (future code paths, # tests) can't reintroduce the double-/v1 404 bug. if ( api_mode == "anthropic_messages" and new_provider in {"opencode-zen", "opencode-go"} and isinstance(base_url, str) and base_url ): base_url = re.sub(r"/v1/?$", "", base_url) old_model = agent.model old_provider = agent.provider # Clear the per-config context_length override so the new model's # actual context window is resolved via get_model_context_length() # instead of inheriting the stale value from the previous model. agent._config_context_length = None # ── Swap core runtime fields ── agent.model = new_model agent.provider = new_provider # Use new base_url when provided; only fall back to current when the # new provider genuinely has no endpoint (e.g. native SDK providers). # Without this guard the old provider's URL (e.g. Ollama's localhost # address) would persist silently after switching to a cloud provider # that returns an empty base_url string. if base_url: agent.base_url = base_url agent.api_mode = api_mode # Invalidate transport cache — new api_mode may need a different transport if hasattr(agent, "_transport_cache"): agent._transport_cache.clear() if api_key: agent.api_key = api_key # ── Build new client ── if api_mode == "anthropic_messages": from agent.anthropic_adapter import ( build_anthropic_client, resolve_anthropic_token, _is_oauth_token, ) # Only fall back to ANTHROPIC_TOKEN when the provider is actually Anthropic. # Other anthropic_messages providers (MiniMax, Alibaba, etc.) must use their own # API key — falling back would send Anthropic credentials to third-party endpoints. _is_native_anthropic = new_provider == "anthropic" effective_key = (api_key or agent.api_key or resolve_anthropic_token() or "") if _is_native_anthropic else (api_key or agent.api_key or "") agent.api_key = effective_key agent._anthropic_api_key = effective_key agent._anthropic_base_url = base_url or getattr(agent, "_anthropic_base_url", None) agent._anthropic_client = build_anthropic_client( effective_key, agent._anthropic_base_url, timeout=get_provider_request_timeout(agent.provider, agent.model), ) agent._is_anthropic_oauth = _is_oauth_token(effective_key) if _is_native_anthropic else False agent.client = None agent._client_kwargs = {} else: effective_key = api_key or agent.api_key effective_base = base_url or agent.base_url agent._client_kwargs = { "api_key": effective_key, "base_url": effective_base, } _sm_timeout = get_provider_request_timeout(agent.provider, agent.model) if _sm_timeout is not None: agent._client_kwargs["timeout"] = _sm_timeout agent.client = agent._create_openai_client( dict(agent._client_kwargs), reason="switch_model", shared=True, ) # ── Re-evaluate prompt caching ── agent._use_prompt_caching, agent._use_native_cache_layout = ( agent._anthropic_prompt_cache_policy( provider=new_provider, base_url=agent.base_url, api_mode=api_mode, model=new_model, ) ) # ── LM Studio: preload before probing context length ── agent._ensure_lmstudio_runtime_loaded() # ── Update context compressor ── if hasattr(agent, "context_compressor") and agent.context_compressor: from agent.model_metadata import get_model_context_length # Re-read custom_providers from live config so per-model # context_length overrides are honored when switching to a # custom provider mid-session (closes #15779). _sm_custom_providers = None try: from hermes_cli.config import load_config, get_compatible_custom_providers _sm_cfg = load_config() _sm_custom_providers = get_compatible_custom_providers(_sm_cfg) except Exception: _sm_custom_providers = None # ``agent.api_key`` may be a callable (Azure Foundry Entra ID # token provider). ``get_model_context_length`` expects a # string for its live-probe paths; for Foundry the context # length normally resolves via config or static catalogs and # never hits a probe, but coerce to empty string defensively. _ctx_api_key = agent.api_key if isinstance(agent.api_key, str) else "" new_context_length = get_model_context_length( agent.model, base_url=agent.base_url, api_key=_ctx_api_key, provider=agent.provider, config_context_length=getattr(agent, "_config_context_length", None), custom_providers=_sm_custom_providers, ) agent.context_compressor.update_model( model=agent.model, context_length=new_context_length, base_url=agent.base_url, api_key=agent.api_key, # context_compressor forwards to call_llm; callable preserved provider=agent.provider, api_mode=agent.api_mode, ) # ── Invalidate cached system prompt so it rebuilds next turn ── agent._cached_system_prompt = None # ── Update _primary_runtime so the change persists across turns ── _cc = agent.context_compressor if hasattr(agent, "context_compressor") and agent.context_compressor else None agent._primary_runtime = { "model": agent.model, "provider": agent.provider, "base_url": agent.base_url, "api_mode": agent.api_mode, "api_key": getattr(agent, "api_key", ""), "client_kwargs": dict(agent._client_kwargs), "use_prompt_caching": agent._use_prompt_caching, "use_native_cache_layout": agent._use_native_cache_layout, "compressor_model": getattr(_cc, "model", agent.model) if _cc else agent.model, "compressor_base_url": getattr(_cc, "base_url", agent.base_url) if _cc else agent.base_url, "compressor_api_key": getattr(_cc, "api_key", "") if _cc else "", "compressor_provider": getattr(_cc, "provider", agent.provider) if _cc else agent.provider, "compressor_context_length": _cc.context_length if _cc else 0, "compressor_threshold_tokens": _cc.threshold_tokens if _cc else 0, } if api_mode == "anthropic_messages": agent._primary_runtime.update({ "anthropic_api_key": agent._anthropic_api_key, "anthropic_base_url": agent._anthropic_base_url, "is_anthropic_oauth": agent._is_anthropic_oauth, }) # ── Reset fallback state ── agent._fallback_activated = False agent._fallback_index = 0 # When the user deliberately swaps primary providers (e.g. openrouter # → anthropic), drop any fallback entries that target the OLD primary # or the NEW one. The chain was seeded from config at agent init for # the original provider — without pruning, a failed turn on the new # primary silently re-activates the provider the user just rejected, # which is exactly what was reported during TUI v2 blitz testing # ("switched to anthropic, tui keeps trying openrouter"). old_norm = (old_provider or "").strip().lower() new_norm = (new_provider or "").strip().lower() fallback_chain = list(getattr(agent, "_fallback_chain", []) or []) if old_norm and new_norm and old_norm != new_norm: fallback_chain = [ entry for entry in fallback_chain if (entry.get("provider") or "").strip().lower() not in {old_norm, new_norm} ] agent._fallback_chain = fallback_chain agent._fallback_model = fallback_chain[0] if fallback_chain else None logging.info( "Model switched in-place: %s (%s) -> %s (%s)", old_model, old_provider, new_model, new_provider, ) def invoke_tool(agent, function_name: str, function_args: dict, effective_task_id: str, tool_call_id: Optional[str] = None, messages: list = None, pre_tool_block_checked: bool = False) -> str: """Invoke a single tool and return the result string. No display logic. Handles both agent-level tools (todo, memory, etc.) and registry-dispatched tools. Used by the concurrent execution path; the sequential path retains its own inline invocation for backward-compatible display handling. """ # Check plugin hooks for a block directive before executing anything. block_message: Optional[str] = None if not pre_tool_block_checked: try: from hermes_cli.plugins import get_pre_tool_call_block_message block_message = get_pre_tool_call_block_message( function_name, function_args, task_id=effective_task_id or "", ) except Exception: pass if block_message is not None: return json.dumps({"error": block_message}, ensure_ascii=False) if function_name == "todo": from tools.todo_tool import todo_tool as _todo_tool return _todo_tool( todos=function_args.get("todos"), merge=function_args.get("merge", False), store=agent._todo_store, ) elif function_name == "session_search": session_db = agent._get_session_db_for_recall() if not session_db: from hermes_state import format_session_db_unavailable return json.dumps({"success": False, "error": format_session_db_unavailable()}) from tools.session_search_tool import session_search as _session_search return _session_search( query=function_args.get("query", ""), role_filter=function_args.get("role_filter"), limit=function_args.get("limit", 3), session_id=function_args.get("session_id"), around_message_id=function_args.get("around_message_id"), window=function_args.get("window", 5), sort=function_args.get("sort"), db=session_db, current_session_id=agent.session_id, ) elif function_name == "memory": target = function_args.get("target", "memory") from tools.memory_tool import memory_tool as _memory_tool result = _memory_tool( action=function_args.get("action"), target=target, content=function_args.get("content"), old_text=function_args.get("old_text"), store=agent._memory_store, ) # Bridge: notify external memory provider of built-in memory writes if agent._memory_manager and function_args.get("action") in {"add", "replace"}: try: agent._memory_manager.on_memory_write( function_args.get("action", ""), target, function_args.get("content", ""), metadata=agent._build_memory_write_metadata( task_id=effective_task_id, tool_call_id=tool_call_id, ), ) except Exception: pass return result elif agent._memory_manager and agent._memory_manager.has_tool(function_name): return agent._memory_manager.handle_tool_call(function_name, function_args) elif function_name == "clarify": from tools.clarify_tool import clarify_tool as _clarify_tool return _clarify_tool( question=function_args.get("question", ""), choices=function_args.get("choices"), callback=agent.clarify_callback, ) elif function_name == "delegate_task": return agent._dispatch_delegate_task(function_args) else: return _ra().handle_function_call( function_name, function_args, effective_task_id, tool_call_id=tool_call_id, session_id=agent.session_id or "", enabled_tools=list(agent.valid_tool_names) if agent.valid_tool_names else None, skip_pre_tool_call_hook=True, ) def repair_tool_call(agent, tool_name: str) -> str | None: """Attempt to repair a mismatched tool name before aborting. Models sometimes emit variants of a tool name that differ only in casing, separators, or class-like suffixes. Normalize aggressively before falling back to fuzzy match: 1. Lowercase direct match. 2. Lowercase + hyphens/spaces -> underscores. 3. CamelCase -> snake_case (TodoTool -> todo_tool). 4. Strip trailing ``_tool`` / ``-tool`` / ``tool`` suffix that Claude-style models sometimes tack on (TodoTool_tool -> TodoTool -> Todo -> todo). Applied twice so double-tacked suffixes like ``TodoTool_tool`` reduce all the way. 5. Fuzzy match (difflib, cutoff=0.7). See #14784 for the original reports (TodoTool_tool, Patch_tool, BrowserClick_tool were all returning "Unknown tool" before). Returns the repaired name if found in valid_tool_names, else None. """ import re from difflib import get_close_matches if not tool_name: return None def _norm(s: str) -> str: return s.lower().replace("-", "_").replace(" ", "_") def _camel_snake(s: str) -> str: return re.sub(r"(? str | None: lc = s.lower() for suffix in ("_tool", "-tool", "tool"): if lc.endswith(suffix): return s[: -len(suffix)].rstrip("_-") return None # Cheap fast-paths first — these cover the common case. lowered = tool_name.lower() if lowered in agent.valid_tool_names: return lowered normalized = _norm(tool_name) if normalized in agent.valid_tool_names: return normalized # Build the full candidate set for class-like emissions. cands: set[str] = {tool_name, lowered, normalized, _camel_snake(tool_name)} # Strip trailing tool-suffix up to twice — TodoTool_tool needs it. for _ in range(2): extra: set[str] = set() for c in cands: stripped = _strip_tool_suffix(c) if stripped: extra.add(stripped) extra.add(_norm(stripped)) extra.add(_camel_snake(stripped)) cands |= extra for c in cands: if c and c in agent.valid_tool_names: return c # Fuzzy match as last resort. matches = get_close_matches(lowered, agent.valid_tool_names, n=1, cutoff=0.7) if matches: return matches[0] return None def sanitize_api_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Fix orphaned tool_call / tool_result pairs before every LLM call. Runs unconditionally — not gated on whether the context compressor is present — so orphans from session loading or manual message manipulation are always caught. """ # --- Role allowlist: drop messages with roles the API won't accept --- filtered = [] for msg in messages: role = msg.get("role") if role not in _ra().AIAgent._VALID_API_ROLES: _ra().logger.debug( "Pre-call sanitizer: dropping message with invalid role %r", role, ) continue filtered.append(msg) messages = filtered surviving_call_ids: set = set() for msg in messages: if msg.get("role") == "assistant": for tc in msg.get("tool_calls") or []: cid = _ra().AIAgent._get_tool_call_id_static(tc) if cid: surviving_call_ids.add(cid) result_call_ids: set = set() for msg in messages: if msg.get("role") == "tool": cid = msg.get("tool_call_id") if cid: result_call_ids.add(cid) # 1. Drop tool results with no matching assistant call orphaned_results = result_call_ids - surviving_call_ids if orphaned_results: messages = [ m for m in messages if not (m.get("role") == "tool" and m.get("tool_call_id") in orphaned_results) ] _ra().logger.debug( "Pre-call sanitizer: removed %d orphaned tool result(s)", len(orphaned_results), ) # 2. Inject stub results for calls whose result was dropped missing_results = surviving_call_ids - result_call_ids if missing_results: patched: List[Dict[str, Any]] = [] for msg in messages: patched.append(msg) if msg.get("role") == "assistant": for tc in msg.get("tool_calls") or []: cid = _ra().AIAgent._get_tool_call_id_static(tc) if cid in missing_results: patched.append({ "role": "tool", "name": _ra().AIAgent._get_tool_call_name_static(tc), "content": "[Result unavailable — see context summary above]", "tool_call_id": cid, }) messages = patched _ra().logger.debug( "Pre-call sanitizer: added %d stub tool result(s)", len(missing_results), ) return messages def looks_like_codex_intermediate_ack( agent, user_message: str, assistant_content: str, messages: List[Dict[str, Any]], ) -> bool: """Detect a planning/ack message that should continue instead of ending the turn.""" if any(isinstance(msg, dict) and msg.get("role") == "tool" for msg in messages): return False assistant_text = agent._strip_think_blocks(assistant_content or "").strip().lower() if not assistant_text: return False if len(assistant_text) > 1200: return False has_future_ack = bool( re.search(r"\b(i['’]ll|i will|let me|i can do that|i can help with that)\b", assistant_text) ) if not has_future_ack: return False action_markers = ( "look into", "look at", "inspect", "scan", "check", "analyz", "review", "explore", "read", "open", "run", "test", "fix", "debug", "search", "find", "walkthrough", "report back", "summarize", ) workspace_markers = ( "directory", "current directory", "current dir", "cwd", "repo", "repository", "codebase", "project", "folder", "filesystem", "file tree", "files", "path", ) user_text = (user_message or "").strip().lower() user_targets_workspace = ( any(marker in user_text for marker in workspace_markers) or "~/" in user_text or "/" in user_text ) assistant_mentions_action = any(marker in assistant_text for marker in action_markers) assistant_targets_workspace = any( marker in assistant_text for marker in workspace_markers ) return (user_targets_workspace or assistant_targets_workspace) and assistant_mentions_action def copy_reasoning_content_for_api(agent, source_msg: dict, api_msg: dict) -> None: """Copy provider-facing reasoning fields onto an API replay message.""" if source_msg.get("role") != "assistant": return # 1. Explicit reasoning_content already set — preserve it verbatim # (includes DeepSeek/Kimi's own space-placeholder written at creation # time, and any valid reasoning content from the same provider). # # Exception: sessions persisted BEFORE #17341 have empty-string # placeholders pinned at creation time. DeepSeek V4 Pro rejects # those with HTTP 400. When the active provider enforces the # thinking-mode echo, upgrade "" → " " on replay so stale history # doesn't 400 the user on the next turn. existing = source_msg.get("reasoning_content") if isinstance(existing, str): if existing == "" and agent._needs_thinking_reasoning_pad(): api_msg["reasoning_content"] = " " else: api_msg["reasoning_content"] = existing return needs_thinking_pad = agent._needs_thinking_reasoning_pad() # 2. Cross-provider poisoned history (#15748): on DeepSeek/Kimi, # if the source turn has tool_calls AND a 'reasoning' field but no # 'reasoning_content' key, the 'reasoning' text was written by a # prior provider (e.g. MiniMax) — DeepSeek's own _build_assistant_message # pins reasoning_content at creation time for tool-call turns, so the # shape (reasoning set, reasoning_content absent, tool_calls present) # is unreachable from same-provider DeepSeek history after this fix. # Inject a single space to satisfy the API without leaking another # provider's chain of thought to DeepSeek/Kimi. Space (not "") # because DeepSeek V4 Pro rejects empty-string reasoning_content # in thinking mode (refs #17341). normalized_reasoning = source_msg.get("reasoning") if ( needs_thinking_pad and source_msg.get("tool_calls") and isinstance(normalized_reasoning, str) and normalized_reasoning ): api_msg["reasoning_content"] = " " return # 3. Healthy session: promote 'reasoning' field to 'reasoning_content' # for providers that use the internal 'reasoning' key. # This must happen before the unconditional empty-string fallback so # genuine reasoning content is not overwritten (#15812 regression in # PR #15478). if isinstance(normalized_reasoning, str) and normalized_reasoning: api_msg["reasoning_content"] = normalized_reasoning return # 4. DeepSeek / Kimi thinking mode: all assistant messages need # reasoning_content. Inject a single space to satisfy the provider's # requirement when no explicit reasoning content is present. Covers # both tool-call turns (already-poisoned history with no reasoning # at all) and plain text turns. Space (not "") because DeepSeek V4 # Pro tightened validation and rejects empty string with HTTP 400 # ("The reasoning content in the thinking mode must be passed back # to the API"). Refs #17341. if needs_thinking_pad: api_msg["reasoning_content"] = " " return # 5. reasoning_content was present but not a string (e.g. None after # context compaction). Don't pass null to the API. api_msg.pop("reasoning_content", None) def _iter_pool_sockets(client: Any): """Yield raw sockets reachable from an OpenAI/httpx client pool. httpcore 1.x stores the concrete HTTP11/HTTP2 connection under ``conn._connection``; older versions exposed stream attributes directly on the pool entry. Keep the traversal defensive because these are private transport internals and vary across httpx/httpcore releases. """ try: http_client = getattr(client, "_client", None) if http_client is None: return transport = getattr(http_client, "_transport", None) if transport is None: return pool = getattr(transport, "_pool", None) if pool is None: return connections = ( getattr(pool, "_connections", None) or getattr(pool, "_pool", None) or [] ) except Exception: return seen: set[int] = set() for conn in list(connections): candidates = [conn] inner = getattr(conn, "_connection", None) if inner is not None: candidates.append(inner) for candidate in candidates: stream = ( getattr(candidate, "_network_stream", None) or getattr(candidate, "_stream", None) ) if stream is None: continue sock = getattr(stream, "_sock", None) if sock is None: get_extra_info = getattr(stream, "get_extra_info", None) if callable(get_extra_info): try: sock = get_extra_info("socket") except Exception: sock = None if sock is None: wrapped = getattr(stream, "stream", None) if wrapped is not None: sock = getattr(wrapped, "_sock", None) if sock is None: # anyio-backed streams expose the raw socket through # SocketAttribute.raw_socket when available. wrapped = getattr(stream, "_stream", None) extra = getattr(wrapped, "extra", None) if callable(extra): try: from anyio.abc import SocketAttribute sock = extra(SocketAttribute.raw_socket) except Exception: sock = None if sock is None: continue marker = id(sock) if marker in seen: continue seen.add(marker) yield sock def cleanup_dead_connections(agent) -> bool: """Detect and clean up dead TCP connections on the primary client. Inspects the httpx connection pool for sockets in unhealthy states (CLOSE-WAIT, errors). If any are found, force-closes all sockets and rebuilds the primary client from scratch. Returns True if dead connections were found and cleaned up. """ client = getattr(agent, "client", None) if client is None: return False try: dead_count = 0 for sock in _iter_pool_sockets(client): # Probe socket health with a non-blocking recv peek import socket as _socket try: sock.setblocking(False) data = sock.recv(1, _socket.MSG_PEEK | _socket.MSG_DONTWAIT) if data == b"": dead_count += 1 except BlockingIOError: pass # No data available — socket is healthy except OSError: dead_count += 1 finally: try: sock.setblocking(True) except OSError: pass if dead_count > 0: _ra().logger.warning( "Found %d dead connection(s) in client pool — rebuilding client", dead_count, ) agent._replace_primary_openai_client(reason="dead_connection_cleanup") return True except Exception as exc: _ra().logger.debug("Dead connection check error: %s", exc) return False def extract_api_error_context(error: Exception) -> Dict[str, Any]: """Extract structured rate-limit details from provider errors.""" context: Dict[str, Any] = {} body = getattr(error, "body", None) payload = None if isinstance(body, dict): payload = body.get("error") if isinstance(body.get("error"), dict) else body if isinstance(payload, dict): reason = payload.get("code") or payload.get("type") or payload.get("error") if isinstance(reason, str) and reason.strip(): context["reason"] = reason.strip() message = payload.get("message") or payload.get("error_description") if isinstance(message, str) and message.strip(): context["message"] = message.strip() for key in ("resets_at", "reset_at"): value = payload.get(key) if value not in {None, ""}: context["reset_at"] = value break retry_after = payload.get("retry_after") if retry_after not in {None, ""} and "reset_at" not in context: try: context["reset_at"] = time.time() + float(retry_after) except (TypeError, ValueError): pass response = getattr(error, "response", None) headers = getattr(response, "headers", None) if headers: retry_after = headers.get("retry-after") or headers.get("Retry-After") if retry_after and "reset_at" not in context: try: context["reset_at"] = time.time() + float(retry_after) except (TypeError, ValueError): pass ratelimit_reset = headers.get("x-ratelimit-reset") if ratelimit_reset and "reset_at" not in context: context["reset_at"] = ratelimit_reset if "message" not in context: raw_message = str(error).strip() if raw_message: context["message"] = raw_message[:500] if "reset_at" not in context: message = context.get("message") or "" if isinstance(message, str): delay_match = re.search(r"quotaResetDelay[:\s\"]+(\\d+(?:\\.\\d+)?)(ms|s)", message, re.IGNORECASE) if delay_match: value = float(delay_match.group(1)) seconds = value / 1000.0 if delay_match.group(2).lower() == "ms" else value context["reset_at"] = time.time() + seconds else: sec_match = re.search( r"retry\s+(?:after\s+)?(\d+(?:\.\d+)?)\s*(?:sec|secs|seconds|s\b)", message, re.IGNORECASE, ) if sec_match: context["reset_at"] = time.time() + float(sec_match.group(1)) return context def apply_pending_steer_to_tool_results(agent, messages: list, num_tool_msgs: int) -> None: """Append any pending /steer text to the last tool result in this turn. Called at the end of a tool-call batch, before the next API call. The steer is appended to the last ``role:"tool"`` message's content with a clear marker so the model understands it came from the user and NOT from the tool itself. Role alternation is preserved — nothing new is inserted, we only modify existing content. Args: messages: The running messages list. num_tool_msgs: Number of tool results appended in this batch; used to locate the tail slice safely. """ if num_tool_msgs <= 0 or not messages: return steer_text = agent._drain_pending_steer() if not steer_text: return # Find the last tool-role message in the recent tail. Skipping # non-tool messages defends against future code appending # something else at the boundary. target_idx = None for j in range(len(messages) - 1, max(len(messages) - num_tool_msgs - 1, -1), -1): msg = messages[j] if isinstance(msg, dict) and msg.get("role") == "tool": target_idx = j break if target_idx is None: # No tool result in this batch (e.g. all skipped by interrupt); # put the steer back so the caller's fallback path can deliver # it as a normal next-turn user message. _lock = getattr(agent, "_pending_steer_lock", None) if _lock is not None: with _lock: if agent._pending_steer: agent._pending_steer = agent._pending_steer + "\n" + steer_text else: agent._pending_steer = steer_text else: existing = getattr(agent, "_pending_steer", None) agent._pending_steer = (existing + "\n" + steer_text) if existing else steer_text return marker = f"\n\nUser guidance: {steer_text}" existing_content = messages[target_idx].get("content", "") if not isinstance(existing_content, str): # Anthropic multimodal content blocks — preserve them and append # a text block at the end. try: blocks = list(existing_content) if existing_content else [] blocks.append({"type": "text", "text": marker.lstrip()}) messages[target_idx]["content"] = blocks except Exception: # Fall back to string replacement if content shape is unexpected. messages[target_idx]["content"] = f"{existing_content}{marker}" else: messages[target_idx]["content"] = existing_content + marker _ra().logger.info( "Delivered /steer to agent after tool batch (%d chars): %s", len(steer_text), steer_text[:120] + ("..." if len(steer_text) > 120 else ""), ) def force_close_tcp_sockets(client: Any) -> int: """Force-close underlying TCP sockets to prevent CLOSE-WAIT accumulation. When a provider drops a connection mid-stream, httpx's ``client.close()`` performs a graceful shutdown which leaves sockets in CLOSE-WAIT until the OS times them out (often minutes). This method walks the httpx transport pool and issues ``socket.shutdown(SHUT_RDWR)`` + ``socket.close()`` to force an immediate TCP RST, freeing the file descriptors. Returns the number of sockets force-closed. """ import socket as _socket closed = 0 try: for sock in _iter_pool_sockets(client): try: sock.shutdown(_socket.SHUT_RDWR) except OSError: pass try: sock.close() except OSError: pass closed += 1 except Exception as exc: _ra().logger.debug("Force-close TCP sockets sweep error: %s", exc) return closed __all__ = [ "convert_to_trajectory_format", "sanitize_tool_call_arguments", "repair_message_sequence", "strip_think_blocks", "recover_with_credential_pool", "try_recover_primary_transport", "drop_thinking_only_and_merge_users", "restore_primary_runtime", "extract_reasoning", "dump_api_request_debug", "anthropic_prompt_cache_policy", "create_openai_client", "switch_model", "invoke_tool", "repair_tool_call", "sanitize_api_messages", "looks_like_codex_intermediate_ack", "copy_reasoning_content_for_api", "cleanup_dead_connections", "extract_api_error_context", "apply_pending_steer_to_tool_results", "_iter_pool_sockets", "force_close_tcp_sockets", ]