d1998c30f7
Review follow-ups on the inline-image guard: - `image/jpg` is the JPEG alias every other image site accepts (vision_message_prep, conversation_compression, image_gen_provider, mcp_tool_content); the Responses guard downgraded it to a text placeholder, losing a valid image. It now counts as JPEG. - The Anthropic converter forwarded data:image/svg+xml (bmp, tiff) verbatim as media_type, which 400s every turn once the part is in history. It now applies the same rule: SVG is rasterized to PNG when a rasterizer exists, any other unsupported inline subtype becomes a text placeholder, and image/jpg is normalized to image/jpeg. Both wire paths share one helper next to the existing supported-set constant in tools/vision_tools_image_prep (import-time deps: hermes_constants only, no cycle). A/B: new Anthropic test and the jpg assertion red on the previous head, green now.
737 lines
37 KiB
Python
737 lines
37 KiB
Python
"""OpenAI-style -> Anthropic Messages API request conversion: model-id normalization, tool
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schemas, and the message list (content blocks, thinking blocks and their signatures,
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tool_use/tool_result pairing, cache_control placement, screenshot eviction, blank-block
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scrubbing). Endpoint predicates come from ``agent/anthropic_endpoints.py`` so this module never
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imports the adapter (no cycle)."""
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import copy
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import json
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import logging
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import re
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from typing import Any, Dict, List, Optional, Tuple
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from agent.image_eviction_policy import outbound_image_retire_count
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from agent.anthropic_endpoints import (
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_is_deepseek_anthropic_endpoint, _is_kimi_family_endpoint, _is_nous_portal_endpoint,
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_is_third_party_anthropic_endpoint, _model_name_is_deepseek_thinking,
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)
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logger = logging.getLogger(__name__)
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_THINKING_TYPES = frozenset(("thinking", "redacted_thinking"))
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_CACHEABLE_TYPES = frozenset(("text", "tool_use"))
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_EMPTY_TEXT_PLACEHOLDER = "(empty)"
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_EMPTY_SCHEMA = {"type": "object", "properties": {}}
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_BEDROCK_REGION_PREFIXES = ("global.", "us.", "eu.", "apac.", "ap.", "au.", "jp.", "ca.", "sa.", "me.", "af.")
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def _block_type(b: Any) -> Any:
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"""``type`` of a dict block, None for non-dicts."""
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return b.get("type") if isinstance(b, dict) else None
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def _has_block_type(blocks: List[Any], types) -> bool:
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return any(_block_type(b) in types for b in blocks)
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def _is_blank_text_block(b: Any) -> bool:
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"""A text block whose ``text`` is not a non-whitespace string (None/int/blank all count) —
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Anthropic 400s on them ("text content blocks must contain non-whitespace text")."""
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return _block_type(b) == "text" and not (isinstance(b.get("text"), str) and b["text"].strip())
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def _cache_control_of(b: Any) -> Optional[Dict[str, Any]]:
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cc = b.get("cache_control") if isinstance(b, dict) else None
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return cc if isinstance(cc, dict) else None
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def _text_block(text: str) -> Dict[str, str]:
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return {"type": "text", "text": text}
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def _text_block_with_citations(text: Any, cits: Any) -> Dict[str, Any]:
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"""Text block carrying ``citations`` only when it is a non-empty list (the only input-valid shape)."""
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block: Dict[str, Any] = _text_block(text)
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if isinstance(cits, list) and cits:
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block["citations"] = cits
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return block
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def _parse_tool_args(raw: Any) -> Any:
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"""JSON-decode a tool_call ``arguments`` string; non-strings pass through, bad JSON -> {}."""
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try:
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return json.loads(raw) if isinstance(raw, str) else raw
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except (json.JSONDecodeError, ValueError):
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return {}
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def _strip_thinking(blocks: List[Any]) -> List[Any]:
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return [b for b in blocks if _block_type(b) not in _THINKING_TYPES]
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def _block_ids(blocks: List[Any], btype: str, key: str) -> set:
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return {b.get(key) for b in blocks if _block_type(b) == btype}
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def _assistant_block_lists(result: List[Dict[str, Any]]):
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"""``(index, message)`` for every assistant message whose content is a block list."""
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return ((i, m) for i, m in enumerate(result) if m.get("role") == "assistant" and isinstance(m.get("content"), list))
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def _carry_cache_control(out: Dict[str, Any], b: Any, *, copy: bool = False) -> Dict[str, Any]:
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"""Carry a dict-valued ``cache_control`` marker from ``b`` onto ``out`` (returned); ``copy``
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shallow-copies it so the caller's dict is never shared with the wire payload."""
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cc = _cache_control_of(b)
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if cc is not None:
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out["cache_control"] = dict(cc) if copy else cc
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return out
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def _split_blank_text_blocks(blocks: List[Any]) -> Tuple[List[Any], Any, List[int]]:
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"""``(kept, relocated_cache_control, dropped_indexes)``: drop blank text blocks, remembering
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the cache_control of the last one dropped so the caller can relocate the breakpoint."""
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dropped = [i for i, blk in enumerate(blocks) if _is_blank_text_block(blk)]
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kept = [blk for i, blk in enumerate(blocks) if i not in dropped]
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relocated = [cc for i in dropped if (cc := _cache_control_of(blocks[i])) is not None]
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return kept, relocated[-1] if relocated else None, dropped
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def _is_bedrock_model_id(model: str) -> bool:
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"""Bedrock ids (``anthropic.claude-opus-4-7``, ``us.anthropic.claude-*``) use dots as namespace
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separators that must be preserved verbatim."""
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return model.lower().startswith(_BEDROCK_REGION_PREFIXES + ("anthropic.",))
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def normalize_model_name(model: str, preserve_dots: bool = False) -> str:
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"""Strip the ``anthropic/`` prefix (case-insensitive) and, unless ``preserve_dots`` (DashScope:
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``qwen3.5-plus``), convert version dots to hyphens for Claude models only (``claude-opus-4.6``
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-> ``claude-opus-4-6``). Bedrock ids keep their namespace dots; non-Anthropic names
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(``gpt-5.4``) keep dots as part of their canonical form."""
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if model.lower().startswith("anthropic/"):
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model = model[len("anthropic/"):]
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if not preserve_dots and not _is_bedrock_model_id(model) and model.lower().startswith(("claude-", "anthropic/")):
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# Only convert dots to hyphens for Anthropic/Claude models. See issue #17171.
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model = model.replace(".", "-")
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return model
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def _sanitize_tool_id(tool_id: str) -> str:
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"""Anthropic requires ids matching [a-zA-Z0-9_-]; replace the rest, never empty."""
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return (re.sub(r"[^a-zA-Z0-9_-]", "_", tool_id) if tool_id else "") or "tool_0"
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def _tool_use_block(tool_id: Any, name: Any, tool_input: Any) -> Dict[str, Any]:
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return {"type": "tool_use", "id": _sanitize_tool_id(tool_id), "name": name, "input": tool_input}
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def _normalize_tool_input_schema(schema: Any) -> Dict[str, Any]:
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"""Normalize a tool schema for Anthropic's validator: collapse nullable unions (``anyOf:
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[{type: string}, {type: null}]`` from Pydantic/MCP optional fields) to the non-null branch —
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optionality is already expressed by ``required``; ``keep_nullable_hint=False`` because the
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OpenAPI ``nullable`` keyword is not recognized. Top-level oneOf/allOf/anyOf are rejected with a
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generic 400, so they are dropped in favour of a plain object."""
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from tools.schema_sanitizer import strip_nullable_unions
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normalized = strip_nullable_unions(schema, keep_nullable_hint=False) if schema else None
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if not isinstance(normalized, dict):
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return dict(_EMPTY_SCHEMA)
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banned = {"oneOf", "allOf", "anyOf"}
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if banned & normalized.keys():
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normalized = {k: v for k, v in normalized.items() if k not in banned}
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normalized.setdefault("type", "object")
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if normalized.get("type") == "object" and not isinstance(normalized.get("properties"), dict):
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normalized = {**normalized, "properties": {}}
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return normalized
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def convert_tools_to_anthropic(tools: List[Dict]) -> List[Dict]:
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"""Convert OpenAI tool definitions to Anthropic format. Duplicate names are dropped with a
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warning (Anthropic hard-400s on them); ``cache_control`` on the OpenAI tool dict is forwarded."""
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result = []
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seen_names: set = set()
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for t in tools or []:
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fn = t.get("function", {})
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name = fn.get("name", "")
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# Defensive dedup: Anthropic rejects requests with duplicate tool names. Upstream injection paths
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# already dedup, but this guard converts a hard API failure into a warning. See: #18478
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if name and name in seen_names:
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logger.warning("convert_tools_to_anthropic: duplicate tool name '%s' — dropping second occurrence", name)
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continue
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if name:
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seen_names.add(name)
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anthropic_tool: Dict[str, Any] = {
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"name": name, "description": fn.get("description", ""),
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"input_schema": _normalize_tool_input_schema(fn.get("parameters") or {}),
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}
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result.append(_carry_cache_control(anthropic_tool, t, copy=True))
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return result
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def _image_block_from_openai_url(url: str) -> Dict[str, Any]:
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"""OpenAI image URL / data URL -> Anthropic ``image`` block. An inline subtype the API rejects
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(svg+xml, bmp, tiff) 400s every replay once in history: an SVG is rasterized to PNG when a
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rasterizer is installed, anything else unsupported becomes a text placeholder."""
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from tools.vision_tools_image_prep import rasterize_svg_data_url, unsupported_inline_image_media_type
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url = str(url or "").strip()
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if not url.startswith("data:"):
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return {"type": "image", "source": {"type": "url", "url": url}}
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unsupported = unsupported_inline_image_media_type(url)
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if unsupported == "image/svg+xml" and (png_url := rasterize_svg_data_url(url)) is not None:
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url, unsupported = png_url, None
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if unsupported is not None:
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return _text_block(f"[image omitted: {unsupported} is not a supported image format]")
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header, _, data = url.partition(",")
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mime_part = header[len("data:"):].split(";", 1)[0].strip()
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media_type = mime_part if mime_part.startswith("image/") else "image/jpeg"
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media_type = "image/jpeg" if media_type.lower() == "image/jpg" else media_type
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return {"type": "image", "source": {"type": "base64", "media_type": media_type, "data": data}}
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def _convert_content_part_to_anthropic(part: Any) -> Optional[Dict[str, Any]]:
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"""Convert one OpenAI-style content part to an Anthropic block (None -> dropped)."""
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if part is None:
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return None
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if not isinstance(part, dict):
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return _text_block(part if isinstance(part, str) else str(part))
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ptype = part.get("type")
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if ptype in ("input_text", "text"):
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# Rebuild from whitelisted fields only: stored SDK text blocks carry output-only siblings
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# (parsed_output, citations=None) that the INPUT schema rejects with 400.
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block = _text_block_with_citations(part.get("text", ""), part.get("citations") if ptype == "text" else None)
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elif ptype in {"image_url", "input_image"}:
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image_value = part.get("image_url", {})
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url = image_value.get("url", "") if isinstance(image_value, dict) else str(image_value or "")
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block = _image_block_from_openai_url(url)
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else:
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block = dict(part)
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if (cache_control := _cache_control_of(part)) is not None:
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block.setdefault("cache_control", dict(cache_control))
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return block
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def _to_plain_data(value: Any, *, _depth: int = 0, _path: Optional[set] = None) -> Any:
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"""Recursively convert SDK objects to plain Python data. ``_path`` tracks ids on the *current*
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recursion path (shared but non-cyclic objects convert normally while true cycles stringify);
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depth is capped at 20."""
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_path = set() if _path is None else _path
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obj_id = id(value)
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if _depth > 20 or obj_id in _path:
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return str(value)
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def rec(v):
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return _to_plain_data(v, _depth=_depth + 1, _path=_path)
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_path.add(obj_id)
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if hasattr(value, "model_dump"):
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try:
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# warnings=False: streaming-accumulator blocks trip pydantic's serializer-mismatch
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# UserWarning, which otherwise leaks to the terminal.
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dumped = value.model_dump(warnings=False)
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except TypeError: # duck-typed model_dump without pydantic's signature
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dumped = value.model_dump()
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result = rec(dumped)
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elif isinstance(value, dict):
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result = {k: rec(v) for k, v in value.items()}
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elif isinstance(value, (list, tuple)):
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result = [rec(v) for v in value]
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elif hasattr(value, "__dict__"):
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result = {k: rec(v) for k, v in vars(value).items() if not k.startswith("_")}
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else:
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result = value
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_path.discard(obj_id)
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return result
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def _extract_preserved_thinking_blocks(message: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Deep-copied thinking/redacted_thinking blocks from ``reasoning_details``."""
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raw_details = message.get("reasoning_details")
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if not isinstance(raw_details, list):
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return []
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return [
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copy.deepcopy(d)
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for d in raw_details
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if isinstance(d, dict) and str(d.get("type", "") or "").strip().lower() in _THINKING_TYPES
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]
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def _convert_content_to_anthropic(content: Any) -> Any:
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"""Convert an OpenAI multimodal content list to Anthropic blocks (non-lists pass through)."""
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if not isinstance(content, list):
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return content
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return [b for b in map(_convert_content_part_to_anthropic, content) if b is not None]
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def _content_parts_to_anthropic_blocks(parts: Any) -> List[Dict[str, Any]]:
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"""Tool-message content parts -> tool_result inner blocks (text + image only, the types
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Anthropic accepts there). Used for multimodal tool results."""
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out: List[Dict[str, Any]] = []
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for block in map(_convert_content_part_to_anthropic, parts if isinstance(parts, list) else []):
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if not block:
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continue
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btype, text_val, src = block.get("type"), block.get("text"), block.get("source")
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if btype == "text" and isinstance(text_val, str) and text_val:
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out.append(_text_block(text_val))
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elif btype == "image" and isinstance(src, dict) and src:
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out.append({"type": "image", "source": src})
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return out
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def _safe_text(text: Any) -> str:
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"""``text`` if non-whitespace, else the placeholder. A blank text block stored in history (e.g.
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by compression) is replayed on every turn and wedges the session with HTTP 400; the placeholder
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is self-healing. Mirrors ``bedrock_adapter._safe_text`` (kept separate on purpose)."""
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text = "" if text is None else str(text)
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return text if text.strip() else _EMPTY_TEXT_PLACEHOLDER
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def _replay_text(b: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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# Drop blank blocks rather than coerce in place: the caller relocates any cache_control and
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# falls back to a placeholder only when nothing survives, so "(empty)" never sits as
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# model-visible noise next to real blocks.
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if _is_blank_text_block(b):
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return None
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return _carry_cache_control(_text_block_with_citations(b["text"], b.get("citations")), b)
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def _replay_thinking(b: Dict[str, Any]) -> Dict[str, Any]:
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out = {"type": "thinking", "thinking": b.get("thinking", "")}
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return {**out, "signature": b["signature"]} if b.get("signature") else out
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def _replay_redacted_thinking(b: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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return {"type": "redacted_thinking", "data": b["data"]} if b.get("data") else None
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def _replay_tool_use(b: Dict[str, Any]) -> Dict[str, Any]:
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return _carry_cache_control(_tool_use_block(b.get("id", ""), b.get("name", ""), b.get("input", {})), b)
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def _replay_image(b: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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src = b.get("source")
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return {"type": "image", "source": src} if isinstance(src, dict) else None
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_REPLAY_SANITIZERS = {
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"text": _replay_text, "thinking": _replay_thinking, "redacted_thinking": _replay_redacted_thinking,
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"tool_use": _replay_tool_use, "image": _replay_image,
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}
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def _sanitize_replay_block(b: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""Whitelist a stored Anthropic block so it is valid as REQUEST input. SDK response blocks carry
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output-only fields the INPUT schema forbids ("Extra inputs are not permitted": ``parsed_output``,
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``caller``, ``citations=None``), and ``_to_plain_data`` captured them verbatim. Whitelist per
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type (not blacklist) so future SDK fields can't reintroduce the bug; unknown types are dropped.
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Returns a clean block or None."""
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if not isinstance(b, dict):
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return None
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sanitizer = _REPLAY_SANITIZERS.get(b.get("type"))
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return sanitizer(b) if sanitizer else None
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def _apply_assistant_cache_control_to_last_cacheable_block(blocks: List[Dict[str, Any]], cache_control: Any) -> None:
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if not isinstance(cache_control, dict):
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return
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for block in reversed(blocks):
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if _block_type(block) in _CACHEABLE_TYPES:
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block.setdefault("cache_control", dict(cache_control))
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break
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def _replay_ordered_blocks(m: Dict[str, Any], ordered_blocks: List[Any]) -> Optional[List[Dict[str, Any]]]:
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"""Interleaved-thinking replay: rebuild the assistant turn from the verbatim block list
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normalize_response stored (only for turns interleaving SIGNED thinking with tool_use).
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Preserves block ORDER; returns None if nothing survives. tool_use ``input`` is re-sourced from
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``tool_calls`` (redacted at storage time) rather than the captured block (raw API response, NOT
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redacted), so a secret the model inlined into a tool call never goes back on the wire."""
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redacted_input_by_id = {
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_sanitize_tool_id(tc.get("id", "")): _parse_tool_args((tc.get("function", {}) or {}).get("arguments", "{}"))
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for tc in m.get("tool_calls", []) or []
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if isinstance(tc, dict)
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}
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replayed: List[Dict[str, Any]] = []
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relocated_cc = None
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dropped_blank_text = False
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for b in ordered_blocks:
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clean = _sanitize_replay_block(b)
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if clean is None:
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dropped_blank_text = dropped_blank_text or _block_type(b) == "text"
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if (cc := _cache_control_of(b)) is not None: # relocate a dropped block's breakpoint
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relocated_cc = cc
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continue
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if clean.get("type") == "tool_use" and (redacted := redacted_input_by_id.get(clean.get("id", ""))) is not None:
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clean["input"] = redacted
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replayed.append(clean)
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# Nothing cacheable survived (e.g. signed thinking + blank text): emit the placeholder so the
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# turn stays schema-valid and a relocated marker has a carrier.
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if not _has_block_type(replayed, _CACHEABLE_TYPES) and (dropped_blank_text or relocated_cc is not None):
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replayed.append(_text_block(_EMPTY_TEXT_PLACEHOLDER))
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if not replayed:
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return None
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_apply_assistant_cache_control_to_last_cacheable_block(replayed, relocated_cc)
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_apply_assistant_cache_control_to_last_cacheable_block(replayed, m.get("cache_control"))
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# prompt_caching marks an assistant turn with text by writing cache_control INTO ``content``
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# (not top-level). This path never reads ``content``, so carry that marker over or the
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# breakpoint is burned rather than relocated.
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msg_content = m.get("content")
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if isinstance(msg_content, list):
|
|
inline_cc = next((cc for cc in map(_cache_control_of, msg_content) if cc is not None), None)
|
|
_apply_assistant_cache_control_to_last_cacheable_block(replayed, inline_cc)
|
|
return replayed
|
|
|
|
|
|
def _convert_assistant_message(m: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Assistant message -> Anthropic content blocks (thinking, text, tool_use, Kimi/DeepSeek
|
|
reasoning_content injection)."""
|
|
# apply_anthropic_cache_control marks an assistant turn with non-empty text by writing cache_control
|
|
# INTO ``content`` (see _apply_cache_marker's list branch), not at the top level. This branch rebuilds
|
|
# the message from ordered_blocks and never reads ``content``, so that marker would be dropped -- and
|
|
# because _can_carry_marker already counted this message as a carrier, the breakpoint is burned rather
|
|
# than relocated. #56195 covered the complementary shape (blank content -> top-level marker); this is
|
|
# the interleaved thinking + preamble-text + tool_use shape.
|
|
content = m.get("content", "")
|
|
ordered_blocks = m.get("anthropic_content_blocks")
|
|
if isinstance(ordered_blocks, list) and ordered_blocks:
|
|
replayed = _replay_ordered_blocks(m, ordered_blocks)
|
|
if replayed:
|
|
return {"role": "assistant", "content": replayed}
|
|
blocks = _extract_preserved_thinking_blocks(m)
|
|
# Blank text blocks are dropped; a cache marker riding on one is relocated onto the last
|
|
# surviving cacheable block (prompt_caching sets cache_control on content[-1], which may be
|
|
# exactly the blank block).
|
|
relocated_cc = None
|
|
if isinstance(content, list):
|
|
kept, relocated_cc, _ = _split_blank_text_blocks(_convert_content_to_anthropic(content))
|
|
blocks.extend(kept)
|
|
elif content and str(content).strip():
|
|
blocks.append(_text_block(str(content)))
|
|
for tc in m.get("tool_calls", []):
|
|
if not tc or not isinstance(tc, dict):
|
|
continue
|
|
fn = tc.get("function", {})
|
|
blocks.append(_tool_use_block(tc.get("id", ""), fn.get("name", ""), _parse_tool_args(fn.get("arguments", "{}"))))
|
|
# Kimi's /coding endpoint requires reasoning_content on replayed tool-call turns — even ""
|
|
# (injected as a fallback upstream). Prepend, since thinking must precede text/tool_use. Skip
|
|
# when reasoning_details already supplied (signed) thinking blocks: a duplicate unsigned one
|
|
# would be downgraded to a spurious text block on the last assistant message.
|
|
# See hermes-agent#13848. Accept empty string "" — _copy_reasoning_content_for_api() injects "" as a
|
|
# tier-3 fallback for Kimi tool-call messages that had no reasoning.
|
|
reasoning_content = m.get("reasoning_content")
|
|
if isinstance(reasoning_content, str) and not _has_block_type(blocks, _THINKING_TYPES):
|
|
blocks.insert(0, {"type": "thinking", "thinking": reasoning_content})
|
|
# Empty assistant content is rejected. Fall back ONLY to the placeholder, never to raw
|
|
# ``content`` — that is the unfiltered blank payload just removed. Markers are applied after
|
|
# the fallback so one from a sole dropped blank block lands on the placeholder.
|
|
effective = blocks or [_text_block(_EMPTY_TEXT_PLACEHOLDER)]
|
|
_apply_assistant_cache_control_to_last_cacheable_block(effective, relocated_cc)
|
|
_apply_assistant_cache_control_to_last_cacheable_block(effective, m.get("cache_control"))
|
|
return {"role": "assistant", "content": effective}
|
|
|
|
|
|
def _tool_result_content(m: Dict[str, Any]) -> Any:
|
|
"""Resolve a tool message's content into tool_result content (blocks or string)."""
|
|
content = m.get("content", "")
|
|
multimodal_blocks: Optional[List[Dict[str, Any]]] = None
|
|
if isinstance(content, dict) and content.get("_multimodal"):
|
|
multimodal_blocks = _content_parts_to_anthropic_blocks(content.get("content") or [])
|
|
if not multimodal_blocks and content.get("text_summary"):
|
|
multimodal_blocks = [_text_block(str(content["text_summary"]))]
|
|
elif isinstance(content, list):
|
|
converted = _content_parts_to_anthropic_blocks(content)
|
|
if _has_block_type(converted, {"image"}):
|
|
multimodal_blocks = converted
|
|
if multimodal_blocks is None: # back-compat: blocks stashed under a private key
|
|
stashed = m.get("_anthropic_content_blocks")
|
|
if isinstance(stashed, list) and stashed:
|
|
text_content = content if isinstance(content, str) and content.strip() else None
|
|
multimodal_blocks = [_text_block(text_content)] + stashed if text_content else list(stashed)
|
|
if multimodal_blocks:
|
|
return multimodal_blocks
|
|
return (content if isinstance(content, str) else json.dumps(content) if content else "") or "(no output)"
|
|
|
|
|
|
def _convert_tool_message_to_result(result: List[Dict[str, Any]], m: Dict[str, Any]) -> None:
|
|
"""Append a tool_result to ``result``, merging into a trailing tool_result user message when
|
|
there is one. Mutates ``result`` in place."""
|
|
tool_result = {
|
|
"type": "tool_result", "tool_use_id": _sanitize_tool_id(m.get("tool_call_id", "")),
|
|
"content": _tool_result_content(m),
|
|
}
|
|
_carry_cache_control(tool_result, m, copy=True)
|
|
last = result[-1] if result else {}
|
|
last_content = last.get("content") if last.get("role") == "user" else None
|
|
if isinstance(last_content, list) and last_content and last_content[0].get("type") == "tool_result":
|
|
last_content.append(tool_result)
|
|
else:
|
|
result.append({"role": "user", "content": [tool_result]})
|
|
|
|
|
|
def _convert_user_message(content: Any) -> Dict[str, Any]:
|
|
"""Validate and convert a user message to Anthropic format."""
|
|
if isinstance(content, list):
|
|
content = _fix_blank_text_blocks_in_list(
|
|
_convert_content_to_anthropic(content), placeholder_text="(empty message)",
|
|
msg_index=-1, role="user", location="_convert_user_message",
|
|
)
|
|
elif not content or (isinstance(content, str) and not content.strip()):
|
|
content = "(empty message)"
|
|
return {"role": "user", "content": content}
|
|
|
|
|
|
def _strip_orphaned_tool_blocks(result: List[Dict[str, Any]]) -> None:
|
|
"""Strip tool_use blocks with no matching tool_result, and vice versa. Compression/truncation
|
|
can remove either side of a pair or insert messages between them. Anthropic requires the
|
|
tool_result in the IMMEDIATELY FOLLOWING user message — a global id match is not enough.
|
|
Mutates ``result`` in place."""
|
|
# Pass 1: tool_use without an adjacent result.
|
|
for i, m in _assistant_block_lists(result):
|
|
tool_use_ids_in_turn = _block_ids(m["content"], "tool_use", "id")
|
|
if not tool_use_ids_in_turn:
|
|
continue
|
|
adjacent_result_ids: set = set()
|
|
if i + 1 < len(result):
|
|
nxt = result[i + 1]
|
|
if nxt.get("role") == "user" and isinstance(nxt.get("content"), list):
|
|
adjacent_result_ids = _block_ids(nxt["content"], "tool_result", "tool_use_id")
|
|
orphaned = tool_use_ids_in_turn - adjacent_result_ids
|
|
if not orphaned:
|
|
continue
|
|
kept = [b for b in m["content"] if not (_block_type(b) == "tool_use" and b.get("id") in orphaned)]
|
|
# A signed thinking block on this turn was signed against the ORIGINAL content and is now
|
|
# dead (400 "thinking blocks in the latest assistant message cannot be modified"). Flag so
|
|
# _manage_thinking_signatures demotes it.
|
|
if len(kept) != len(m["content"]) and _has_block_type(m["content"], _THINKING_TYPES):
|
|
m["_thinking_signature_invalidated"] = True
|
|
m["content"] = kept if kept else [_text_block("(tool call removed)")]
|
|
# Pass 2: tool_result whose tool_use no longer exists anywhere.
|
|
surviving_tool_use_ids: set = set()
|
|
for _, m in _assistant_block_lists(result):
|
|
surviving_tool_use_ids |= _block_ids(m["content"], "tool_use", "id")
|
|
for m in result:
|
|
if m.get("role") != "user" or not isinstance(m.get("content"), list):
|
|
continue
|
|
new_content = [
|
|
b for b in m["content"] if _block_type(b) != "tool_result" or b.get("tool_use_id") in surviving_tool_use_ids
|
|
]
|
|
if len(new_content) != len(m["content"]):
|
|
m["content"] = new_content if new_content else [_text_block("(tool result removed)")]
|
|
|
|
|
|
def _concat_content(prev: Any, curr: Any) -> List[Any]:
|
|
"""Merge two message contents into one block list, each side's blocks kept intact (a string
|
|
becomes its own text block). Strings are never joined: the first turn's bytes must equal what a
|
|
later request replays as a standalone turn, or the prompt-cache prefix diverges at that block
|
|
(MoA appends per-turn guidance after ``user(task)`` on iteration 1 and replays ``user(task)``
|
|
alone on iteration 2 — #112358)."""
|
|
as_blocks = lambda c: [_text_block(c)] if isinstance(c, str) else list(c) # noqa: E731
|
|
return as_blocks(prev) + as_blocks(curr)
|
|
|
|
|
|
def _merge_consecutive_roles(result: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
|
"""Merge consecutive same-role messages to enforce alternation. Returns a new list."""
|
|
fixed: List[Dict[str, Any]] = []
|
|
for m in result:
|
|
if not (fixed and fixed[-1]["role"] == m["role"]):
|
|
fixed.append(m)
|
|
continue
|
|
if m["role"] != "user":
|
|
# Keep the orphan-strip flag visible to _manage_thinking_signatures.
|
|
if m.get("_thinking_signature_invalidated"):
|
|
fixed[-1]["_thinking_signature_invalidated"] = True
|
|
# The second message's thinking blocks were signed against a different turn boundary
|
|
# and become invalid once merged.
|
|
if isinstance(m["content"], list):
|
|
m["content"] = _strip_thinking(m["content"])
|
|
fixed[-1]["content"] = _concat_content(fixed[-1]["content"], m["content"])
|
|
return fixed
|
|
|
|
|
|
def _keep_valid_latest_thinking(content: List[Any], signature_dead: bool) -> List[Any]:
|
|
"""Latest assistant turn on direct Anthropic: keep signed thinking, demote unsigned to text so
|
|
the reasoning isn't lost. If orphan-stripping mutated THIS turn every signature is dead (and a
|
|
bare signed block with no tool_use is also invalid), so demote ALL of them."""
|
|
new_content = []
|
|
for b in content:
|
|
if _block_type(b) not in _THINKING_TYPES:
|
|
new_content.append(b)
|
|
continue
|
|
is_redacted = b.get("type") == "redacted_thinking"
|
|
signed = b.get("data") if is_redacted else b.get("signature") # redacted 'data' IS the signature
|
|
if signed and not signature_dead:
|
|
new_content.append(b)
|
|
elif (signature_dead or not is_redacted) and b.get("thinking"):
|
|
new_content.append(_text_block(b["thinking"])) # demote to plain text; dataless redacted dropped
|
|
return new_content
|
|
|
|
|
|
def _manage_thinking_signatures(result: List[Dict[str, Any]], base_url: str | None, model: str | None) -> None:
|
|
"""Strip or preserve thinking blocks per endpoint. Mutates ``result`` in place.
|
|
|
|
Anthropic signs thinking blocks against the full turn; any upstream mutation invalidates them
|
|
(400 "Invalid signature in thinking block"), so on direct Anthropic only the LATEST assistant
|
|
turn keeps signed blocks. Signatures are proprietary: third-party endpoints strip all thinking.
|
|
Kimi replays as-is; DeepSeek needs unsigned blocks round-tripped but rejects signed ones. Nous
|
|
Portal proxies Claude with sticky sessions and validates the same signatures, so it takes the
|
|
native path despite not being anthropic.com.
|
|
"""
|
|
is_third_party = _is_third_party_anthropic_endpoint(base_url) and not _is_nous_portal_endpoint(base_url)
|
|
is_kimi = _is_kimi_family_endpoint(base_url, model)
|
|
is_deepseek = _is_deepseek_anthropic_endpoint(base_url) or (
|
|
is_third_party and _model_name_is_deepseek_thinking(model)
|
|
)
|
|
last_assistant_idx = next((i for i in range(len(result) - 1, -1, -1) if result[i].get("role") == "assistant"), None)
|
|
for idx, m in _assistant_block_lists(result):
|
|
if is_kimi:
|
|
pass # shared cleanup below still strips cache markers + the flag
|
|
elif is_deepseek:
|
|
# Strip signed (or redacted-with-data), keep unsigned.
|
|
new_content = [
|
|
b for b in m["content"]
|
|
if _block_type(b) not in _THINKING_TYPES or not (b.get("signature") or b.get("data"))
|
|
]
|
|
m["content"] = new_content or [_text_block("(empty)")]
|
|
elif is_third_party or idx != last_assistant_idx:
|
|
m["content"] = _strip_thinking(m["content"]) or [_text_block("(thinking elided)")]
|
|
else:
|
|
new_content = _keep_valid_latest_thinking(m["content"], bool(m.get("_thinking_signature_invalidated")))
|
|
m["content"] = new_content or [_text_block("(empty)")]
|
|
# cache_control on thinking blocks interferes with signature validation.
|
|
for b in m["content"]:
|
|
if _block_type(b) in _THINKING_TYPES:
|
|
b.pop("cache_control", None)
|
|
m.pop("_thinking_signature_invalidated", None) # internal flag, never on the wire
|
|
|
|
|
|
def _evict_old_screenshots(result: List[Dict[str, Any]]) -> None:
|
|
"""Retire screenshot payloads once the request would cross the API's per-request image limit.
|
|
|
|
Mutates ``result`` in place. This wire pass has no byte sizes, so it enforces the block
|
|
ceiling only; the auxiliary Anthropic client (``agent.auxiliary_client`` via
|
|
``anthropic_adapter.build_anthropic_kwargs``) reaches it without the compressor's
|
|
send-path pass, so it must hold the invariant alone. Policy: :mod:`agent.image_eviction_policy`.
|
|
"""
|
|
reserved = sum(
|
|
1
|
|
for msg in result
|
|
for block in (msg.get("content") if isinstance(msg.get("content"), list) else [])
|
|
if _block_type(block) == "image"
|
|
)
|
|
# Parallel tool calls land as sibling tool_result blocks inside ONE user message
|
|
# (oldest first), so the inner walk must also run newest -> oldest or a batch that
|
|
# ends mid-message retires the newest frames instead of the oldest (#103217).
|
|
carriers = [
|
|
(block, sum(1 for b in block["content"] if _block_type(b) == "image"))
|
|
for msg in reversed(result)
|
|
for block in reversed(msg.get("content") if isinstance(msg.get("content"), list) else [])
|
|
if _block_type(block) == "tool_result"
|
|
and isinstance(block.get("content"), list)
|
|
and _has_block_type(block["content"], {"image"})
|
|
]
|
|
retire = outbound_image_retire_count([n for _, n in carriers], reserved)
|
|
for block, _ in carriers[len(carriers) - retire:]:
|
|
placeholder = _text_block("[screenshot removed to save context]")
|
|
block["content"] = [
|
|
placeholder if _block_type(b) == "image" else b for b in block["content"]
|
|
]
|
|
|
|
|
|
def _ensure_leading_user_turn(result: List[Dict[str, Any]]) -> None:
|
|
"""Anthropic requires messages[0].role == user; prepend a placeholder turn otherwise. A second
|
|
auto-compaction can leave a role=assistant summary first, which the API rejects (often masked
|
|
as a misleading tool_use/tool_result 400). The filler must be non-whitespace text or it trades
|
|
that 400 for the blank-block one.
|
|
|
|
The inserted text block must be non-whitespace: Anthropic separately rejects any text content block
|
|
whose text is empty or whitespace-only ("text content blocks must contain non-whitespace text"), so a
|
|
single space here traded the "leading assistant turn" 400 for that one (#69512 class). Uses the same
|
|
placeholder as every other synthesized filler block in this module for consistency.
|
|
"""
|
|
if result and result[0].get("role") != "user":
|
|
result.insert(0, {"role": "user", "content": [_text_block(_EMPTY_TEXT_PLACEHOLDER)]})
|
|
|
|
|
|
def _fix_blank_text_blocks_in_list(
|
|
blocks: List[Any], *, placeholder_text: str, msg_index: int, role: Any, location: str
|
|
) -> List[Any]:
|
|
"""Drop blank text blocks; relocate any cache_control they carried onto the last surviving
|
|
cacheable block; if nothing survives, substitute one placeholder block (carrying the relocated
|
|
marker). Non-text blocks and order are untouched. Returns a new list; logs structure only."""
|
|
kept, relocated_cache_control, dropped = _split_blank_text_blocks(blocks)
|
|
for block_index in dropped:
|
|
logger.warning(
|
|
"Pre-call sanitizer: dropped blank text content block "
|
|
"(message_index=%d role=%s location=%s block_index=%d block_type=text)",
|
|
msg_index, role, location, block_index,
|
|
)
|
|
kept = kept or [_text_block(placeholder_text)]
|
|
_apply_assistant_cache_control_to_last_cacheable_block(kept, relocated_cache_control)
|
|
return kept
|
|
|
|
|
|
def _scrub_blank_text_blocks(result: List[Dict[str, Any]]) -> None:
|
|
"""Final boundary guard against blank text blocks (HTTP 400 "text content blocks must contain
|
|
non-whitespace text"), including inside tool_result content. Runs LAST so a blank block from
|
|
any current or future producer never reaches the wire. Mutates ``result`` in place."""
|
|
for msg_index, msg in enumerate(result):
|
|
if not isinstance(msg, dict):
|
|
continue
|
|
role = msg.get("role")
|
|
content = msg.get("content")
|
|
if not isinstance(content, list) or not content:
|
|
continue
|
|
msg["content"] = _fix_blank_text_blocks_in_list(
|
|
content, placeholder_text=_EMPTY_TEXT_PLACEHOLDER if role == "assistant" else "(empty message)",
|
|
msg_index=msg_index, role=role, location="content",
|
|
)
|
|
for blk in msg["content"]:
|
|
inner = blk.get("content") if _block_type(blk) == "tool_result" else None
|
|
if isinstance(inner, list) and inner:
|
|
blk["content"] = _fix_blank_text_blocks_in_list(
|
|
inner, placeholder_text="(no output)", msg_index=msg_index, role=role, location="tool_result",
|
|
)
|
|
|
|
|
|
def _convert_system_content(content: Any) -> Any:
|
|
"""System message content -> Anthropic ``system`` param (str, or block list when cache_control
|
|
is present). With cache markers the blocks are copied (never mutating the caller's dicts) and
|
|
blank text is replaced by the placeholder: Anthropic rejects blank system blocks too, and a
|
|
blank block carrying a breakpoint can't simply be dropped."""
|
|
if not isinstance(content, list):
|
|
return content
|
|
if not any(p.get("cache_control") for p in content if isinstance(p, dict)):
|
|
return "\n".join(p["text"] for p in content if p.get("type") == "text")
|
|
return [
|
|
{**p, "text": _EMPTY_TEXT_PLACEHOLDER} if _is_blank_text_block(p) and isinstance(p.get("text"), str) else p
|
|
for p in content
|
|
if isinstance(p, dict)
|
|
]
|
|
|
|
|
|
def convert_messages_to_anthropic(
|
|
messages: List[Dict], base_url: str | None = None, model: str | None = None
|
|
) -> Tuple[Optional[Any], List[Dict]]:
|
|
"""Convert OpenAI-format messages to Anthropic format -> ``(system, messages)``. System is
|
|
extracted into its own param (a string, or a block list when cache_control is present).
|
|
``base_url``/``model`` drive thinking-signature policy — third-party endpoints strip signatures
|
|
(proprietary, they 400 on them); Kimi-family endpoints/models keep unsigned
|
|
reasoning_content-derived blocks, which Kimi requires even when empty."""
|
|
system = None
|
|
result: List[Dict[str, Any]] = []
|
|
for m in messages:
|
|
role = m.get("role", "user")
|
|
if role == "system":
|
|
system = _convert_system_content(m.get("content", ""))
|
|
elif role == "assistant":
|
|
result.append(_convert_assistant_message(m))
|
|
elif role == "tool":
|
|
_convert_tool_message_to_result(result, m)
|
|
else:
|
|
result.append(_convert_user_message(m.get("content", "")))
|
|
_strip_orphaned_tool_blocks(result)
|
|
result = _merge_consecutive_roles(result)
|
|
_ensure_leading_user_turn(result)
|
|
_manage_thinking_signatures(result, base_url, model)
|
|
_evict_old_screenshots(result)
|
|
_scrub_blank_text_blocks(result)
|
|
return system, result
|