mirror of
https://github.com/hansjone/oclaw.git
synced 2026-10-10 16:13:36 +08:00
feat: plan agent v2, global chat mode, and user-mode v2 flag sync
- Add runtime/plan_agent_v2 package and shims; gateway/direct_loop/WS wiring - Admin chat: interaction mode and specialist only in user menu; session API stores memory_mode and execution_mode only - POST /admin/api/chat/user-mode mirrors plan_agent_version to AIA_EXPERT_PLAN_AGENT_V2_ENABLED (v2 to 1, v1 to 0) - Composer cleanup (hidden mode select, no reasoning toggle in meta bar); tests and _local/system.env.example Co-authored-by: Cursor <cursoragent@cursor.com>
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parent
ae44cbcad5
commit
37522f492a
49 changed files with 4146 additions and 238 deletions
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@ -35,6 +35,8 @@ _DIRECT_LOOP_OC_STAGE: dict[str, str] = {
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"tool_pairing_guard": "tool_pairing_guard",
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}
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_THINK_BLOCK_RE = re.compile(r"<(think|redacted_thinking)>\s*(.*?)\s*</\1>\s*", flags=re.IGNORECASE | re.DOTALL)
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_DSML_INVOKE_NAME_RE = re.compile(r"invoke\s+name\s*=\s*['\"]([^'\"\s>]+)['\"]", flags=re.IGNORECASE)
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_JSON_TOOL_NAME_RE = re.compile(r"['\"]name['\"]\s*:\s*['\"]([^'\"\s]{1,120})['\"]", flags=re.IGNORECASE)
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_TOOL_WIRE_CACHE_LOCK = threading.Lock()
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_TOOL_WIRE_CACHE: dict[str, tuple[float, list[dict[str, Any]]]] = {}
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_TOOL_WIRE_CACHE_TTL_SEC = 300.0
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@ -54,6 +56,16 @@ def _safe_int(raw: Any, default: int, *, min_value: int = 1, max_value: int = 2_
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return min(value, max_value)
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def _safe_nonneg_int(raw: Any, default: int, *, max_value: int = 2_000_000) -> int:
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try:
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value = int(raw)
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except Exception:
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return max(0, int(default))
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if value < 0:
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return max(0, int(default))
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return min(value, max_value)
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def _oclaw_config_path() -> Path:
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raw = str(os.getenv("AIA_OCLAW_CONFIG_PATH") or "").strip()
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if raw:
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@ -820,6 +832,163 @@ def _tool_names_for_trace(tools: list[dict[str, Any]]) -> list[str]:
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return out
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def _chat_with_empty_body_retry(
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*,
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model: Any,
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msgs: list[dict[str, Any]],
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llm_tools: list[dict[str, Any]],
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on_token: Optional[Callable[[str], None]],
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on_progress: Optional[Callable[[str], None]],
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progress_label: str = "oclaw: think",
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) -> Any:
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# Empty assistant body can occur transiently at upstream gateways.
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# Retry until non-empty (bounded by retry count and total timeout).
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retry_max = _safe_nonneg_int(os.getenv("AIA_EMPTY_ASSISTANT_RETRY_MAX"), 1, max_value=3)
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retry_delay_ms = _safe_nonneg_int(os.getenv("AIA_EMPTY_ASSISTANT_RETRY_DELAY_MS"), 1200, max_value=15_000)
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retry_total_timeout_ms = _safe_nonneg_int(os.getenv("AIA_EMPTY_ASSISTANT_RETRY_TOTAL_TIMEOUT_MS"), 30_000, max_value=300_000)
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started = time.perf_counter()
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retries_done = 0
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resp = model.chat(msgs, llm_tools, on_token=on_token)
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while True:
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content = str(getattr(resp, "content", "") or "")
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tool_calls = list(getattr(resp, "tool_calls", []) or [])
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textual_tool_intent = (not tool_calls) and bool(_extract_textual_tool_intent_names(content))
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if (content.strip() or tool_calls) and not textual_tool_intent:
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return resp
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elapsed_ms = int((time.perf_counter() - started) * 1000.0)
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if retries_done >= retry_max or elapsed_ms >= retry_total_timeout_ms:
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return resp
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if textual_tool_intent:
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if on_progress:
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on_progress(f"{progress_label} retry-native-tool-calls ({retries_done + 1}/{retry_max})…")
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repair_msgs = list(msgs) + [
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{
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"role": "system",
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"content": (
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"Do not output textual tool intent/templates (DSML/XML/JSON). "
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"If a tool is needed, return native tool_calls only."
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),
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}
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]
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retries_done += 1
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resp = model.chat(repair_msgs, llm_tools, on_token=on_token)
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continue
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if on_progress:
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on_progress(f"{progress_label} retry-empty ({retries_done + 1}/{retry_max})…")
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if retry_delay_ms > 0:
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time.sleep(float(retry_delay_ms) / 1000.0)
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retries_done += 1
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resp = model.chat(msgs, llm_tools, on_token=on_token)
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def _extract_dsml_invoke_names(text: str) -> list[str]:
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raw = str(text or "")
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if not raw:
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return []
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out: list[str] = []
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seen: set[str] = set()
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for m in _DSML_INVOKE_NAME_RE.finditer(raw):
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nm = str(m.group(1) or "").strip()
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if not nm or nm in seen:
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continue
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seen.add(nm)
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out.append(nm)
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if len(out) >= 8:
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break
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return out
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def _extract_textual_tool_intent_names(text: str) -> list[str]:
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raw = str(text or "")
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if not raw:
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return []
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lower = raw.lower()
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marker_hit = ("tool_calls" in lower) or ("invoke name" in lower) or ("parameter name" in lower)
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if not marker_hit:
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return []
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out: list[str] = []
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seen: set[str] = set()
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for nm in _extract_dsml_invoke_names(raw):
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key = str(nm or "").strip()
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if key and key not in seen:
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seen.add(key)
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out.append(key)
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if len(out) < 8:
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for m in _JSON_TOOL_NAME_RE.finditer(raw):
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nm = str(m.group(1) or "").strip()
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if not nm or nm in seen:
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continue
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seen.add(nm)
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out.append(nm)
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if len(out) >= 8:
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break
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if out:
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return out
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return ["unknown_tool"]
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def _persist_dsml_protocol_mismatch_step(
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*,
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store: Any,
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session_id: str,
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turn_uuid: str,
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assistant_text: str,
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invoke_names: list[str],
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) -> _LoopStepResult:
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names = [str(x or "").strip() for x in (invoke_names or []) if str(x or "").strip()]
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if not names:
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names = ["unknown_tool"]
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stored_tool_calls: list[dict[str, Any]] = []
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for nm in names:
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stored_tool_calls.append(
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{
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"id": f"call_dsml_{uuid.uuid4().hex}",
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"name": nm,
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"arguments": {},
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"thought_signature": None,
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}
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)
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assistant_row = store.add_message(
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session_id=session_id,
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role="assistant",
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content="",
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tool_calls=stored_tool_calls,
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turn_uuid=turn_uuid,
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event_type="tool_call",
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event_payload={
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"protocol_mismatch": "textual_tool_intent",
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"raw_excerpt": str(assistant_text or "")[:2000],
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},
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)
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for tc in stored_tool_calls:
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tcid = str(tc.get("id") or "").strip()
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tname = str(tc.get("name") or "").strip() or "unknown_tool"
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tool_result = {
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"ok": False,
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"error_code": "model_protocol_mismatch_dsml",
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"error": "model_returned_textual_tool_intent_instead_of_native_tool_calls",
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"detail": {"tool_name": tname},
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}
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store.add_message(
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session_id=session_id,
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role="tool",
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content=_json_dumps_safe(tool_result),
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tool_calls={
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"tool_call_id": tcid,
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"name": tname,
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"assistant_message_id": int(getattr(assistant_row, "id", 0) or 0),
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},
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turn_uuid=turn_uuid,
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event_type="tool_result",
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event_payload={"tool_name": tname, "protocol_mismatch": "textual_tool_intent"},
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)
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return _LoopStepResult(
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assistant_text="",
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llm_tool_calls=[],
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assistant_msg_id=int(getattr(assistant_row, "id", 0) or 0),
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)
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def _persist_assistant_step(
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*,
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store: Any,
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@ -843,10 +1012,8 @@ def _persist_assistant_step(
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reasoning_chunks, assistant_body = _split_reasoning_and_body(assistant_text, explicit_reasoning=reasoning_text)
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reasoning_full = "\n".join([str(x or "").strip() for x in reasoning_chunks if str(x or "").strip()]).strip()
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if not str(assistant_body or "").strip() and not stored_tool_calls:
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# Provider/model can occasionally return an empty body; persist a visible stub
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# so UI doesn't look "stuck" and operators can diagnose from history.
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assistant_body = "（空响应）模型返回了空内容，请重试一次；若持续出现，请检查模型网关/上游返回。"
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# Keep empty body as-is when model returns nothing and there are no tool calls.
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# The UI should treat this as an invisible intermediate/final empty response.
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if not thinking_mode_enabled:
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for idx, chunk in enumerate(reasoning_chunks):
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store.add_message(
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@ -1022,20 +1189,37 @@ def run_oclaw_direct_loop(
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lang=lang,
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wire_policy_role=wire_policy_role,
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)
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resp = model.chat(msgs, llm_tools, on_token=on_token)
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resp = _chat_with_empty_body_retry(
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model=model,
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msgs=msgs,
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llm_tools=llm_tools,
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on_token=on_token,
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on_progress=on_progress,
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progress_label="oclaw: think",
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)
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assistant_text = str(getattr(resp, "content", "") or "")
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reasoning_text = str(getattr(resp, "reasoning_content", "") or "")
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llm_tool_calls = list(getattr(resp, "tool_calls", []) or [])
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textual_tool_intent_names = _extract_textual_tool_intent_names(assistant_text) if not llm_tool_calls else []
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step = _persist_assistant_step(
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store=store,
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session_id=session_id,
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turn_uuid=turn_uuid,
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assistant_text=assistant_text,
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reasoning_text=reasoning_text,
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llm_tool_calls=llm_tool_calls,
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thinking_mode_enabled=bool(getattr(model, "thinking_mode_enabled", False)),
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)
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if textual_tool_intent_names:
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step = _persist_dsml_protocol_mismatch_step(
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store=store,
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session_id=session_id,
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turn_uuid=turn_uuid,
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assistant_text=assistant_text,
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invoke_names=textual_tool_intent_names,
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)
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else:
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step = _persist_assistant_step(
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store=store,
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session_id=session_id,
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turn_uuid=turn_uuid,
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assistant_text=assistant_text,
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reasoning_text=reasoning_text,
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llm_tool_calls=llm_tool_calls,
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thinking_mode_enabled=bool(getattr(model, "thinking_mode_enabled", False)),
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)
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final_text = step.assistant_text
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if not step.llm_tool_calls:
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break
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@ -1109,7 +1293,14 @@ def run_oclaw_direct_loop(
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active_turn_uuid=turn_uuid,
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)
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# Final pass forbids extra tool calls; model must synthesize answer.
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resp = model.chat(msgs, [], on_token=on_token)
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resp = _chat_with_empty_body_retry(
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model=model,
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msgs=msgs,
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llm_tools=[],
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on_token=on_token,
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on_progress=on_progress,
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progress_label="oclaw: finalize",
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)
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step = _persist_assistant_step(
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store=store,
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session_id=session_id,
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