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重构主控编排与运行时预热链路，统一工作区提示词/专家调度协议并补齐 wiki 记忆注入与写回闭环。

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同时收敛启动与运维脚本默认行为（含 wiki worker）、更新 Admin 可观测性与相关测试，降低首轮时延并提高运行稳定性。

Made-with: Cursor
This commit is contained in:
oliver 2026-04-26 08:34:33 +08:00
parent 4a23b715a2
commit dbbe3add6a
14438 changed files with 2693620 additions and 2546 deletions
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20
runtime/chat/agent_messages.py
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@ -21,6 +21,7 @@ from oclaw.runtime.relay_pointer import parse_pointer_uri
logger = logging.getLogger(__name__)
_THINK_BLOCK_RE = re.compile(r"<think>\s*(.*?)\s*</think>\s*", flags=re.IGNORECASE | re.DOTALL)
_TOOL_CONTEXT_RESULT_MAX_CHARS = 50
def _replay_recent_tool_rounds() -> int:
@ -175,12 +176,23 @@ def _summarize_unpaired_tool_content(raw: str, *, cap: int) -> str:
return s
def _truncate_tool_context(text: str, *, lang: str) -> str:
s = str(text or "").strip()
if not s:
return s
if len(s) <= _TOOL_CONTEXT_RESULT_MAX_CHARS:
return s
tip = "…（详情请重新阅读）" if not str(lang or "").startswith("en") else "... (details truncated, please re-read)"
return s[:_TOOL_CONTEXT_RESULT_MAX_CHARS] + tip
def build_llm_messages(
*,
store_messages: list[Any],
system_prompt: str,
model: ChatModel,
lang: str,
tool_context_truncate_enabled: bool = True,
) -> list[dict[str, Any]]:
"""把 DB 中的消息序列转换为 LLM messages。"""
out: list[dict[str, Any]] = [{"role": "system", "content": (system_prompt or "").strip()}]
@ -427,6 +439,8 @@ def build_llm_messages(
r0 = getattr(m, "content", "") or ""
cap0 = tool_llm_message_max_chars()
pretty = _summarize_unpaired_tool_content(r0, cap=cap0)
if tool_context_truncate_enabled:
pretty = _truncate_tool_context(pretty, lang=lang)
out.append(
{
"role": "assistant",
@ -457,6 +471,10 @@ def build_llm_messages(
tool_content_out = raw_tc_content[: max(1, cap - 80)] + "\n...<truncated>"
except Exception:
tool_content_out = raw_tc_content[: max(1, cap - 80)] + "\n...<truncated>"
if tool_context_truncate_enabled:
# Preserve explicit guard markers from upstream context guards.
if "_tool_result_guarded" not in str(tool_content_out or ""):
tool_content_out = _truncate_tool_context(tool_content_out, lang=lang)
tool_row: dict[str, Any] = {
"role": "tool",
"tool_call_id": tool_call_id,
@ -476,6 +494,8 @@ def build_llm_messages(
r = getattr(m, "content", "") or ""
cap2 = tool_llm_message_max_chars()
pretty2 = _summarize_unpaired_tool_content(r, cap=cap2)
if tool_context_truncate_enabled:
pretty2 = _truncate_tool_context(pretty2, lang=lang)
out.append(
{
"role": "assistant",