初始化：独立 oclaw 仓库首提交

- 在 oclaw/ 下重新初始化 Git 仓库
- 补齐子仓库 .gitignore，避免提交本地运行态数据（_local、node_modules、logs 等）
- 提交当前工程代码与配置

Made-with: Cursor

platform/config/passwords.py

@@ -0,0 +1,21 @@
+"""访问密码解析（环境变量 + 数据库）；Streamlit secrets 在认证模块中处理。"""
+
+from __future__ import annotations
+
+import os
+
+from oclaw.platform.persistence.sqlite_store import SqliteStore
+
+
+def load_expected_password(store: SqliteStore, *, extra_candidate: str | None = None) -> str | None:
+    pwd = (os.getenv("AIA_ASSISTANT_PASSWORD") or "").strip()
+    if not pwd:
+        pwd = (os.getenv("OPS_ASSISTANT_PASSWORD") or "").strip()
+    if not pwd and extra_candidate:
+        pwd = extra_candidate.strip()
+    if not pwd:
+        pwd = (store.get_secret("auth_password") or "").strip()
+    return pwd if pwd else None
+
+
+__all__ = ["load_expected_password"]

platform/config/paths.py

@@ -0,0 +1,194 @@
+"""仓库与数据文件路径解析（与框架无关，供 ``db`` / UI 等共用）。"""
+
+from __future__ import annotations
+
+import os
+import shutil
+import sqlite3
+import sys
+import time
+import warnings
+from pathlib import Path
+
+
+def _project_root() -> Path:
+    if getattr(sys, "frozen", False):
+        return Path(sys.executable).resolve().parent
+    return Path(__file__).resolve().parents[3]
+
+
+PROJECT_ROOT = _project_root()
+_DATA_MIGRATION_DONE = False
+
+
+def _canonical_data_root() -> Path:
+    return (PROJECT_ROOT / "oclaw" / "data").resolve()
+
+
+def _legacy_platform_data_root() -> Path:
+    return (PROJECT_ROOT / "src" / "platform" / "data").resolve()
+
+
+def _legacy_root_data_root() -> Path:
+    return (PROJECT_ROOT / "data").resolve()
+
+
+def _backup_keep_count() -> int:
+    raw = (os.getenv("AIA_ASSISTANT_PREMERGE_BACKUP_KEEP") or os.getenv("OPS_ASSISTANT_PREMERGE_BACKUP_KEEP") or "").strip()
+    if not raw:
+        return 3
+    try:
+        val = int(raw)
+    except Exception:
+        return 3
+    return max(0, val)
+
+
+def _cleanup_pre_merge_backups() -> None:
+    root = _canonical_data_root()
+    if not root.exists():
+        return
+    keep = _backup_keep_count()
+    backups = sorted([p for p in root.glob("_pre_merge_sqlite_*") if p.is_dir()], key=lambda p: p.name, reverse=True)
+    for old in backups[keep:]:
+        try:
+            shutil.rmtree(old)
+        except Exception as exc:
+            warnings.warn(f"自动清理旧备份失败（已跳过）: {old} ({exc})", RuntimeWarning, stacklevel=1)
+
+
+def attachments_dir() -> Path:
+    p = (_canonical_data_root() / "attachments").resolve()
+    p.mkdir(parents=True, exist_ok=True)
+    return p
+
+
+def _sqlite_sidecars(main: Path) -> list[Path]:
+    return [main, Path(str(main) + "-wal"), Path(str(main) + "-shm")]
+
+
+def _sqlite_chat_session_count(db: Path) -> int | None:
+    if not db.exists():
+        return None
+    try:
+        conn = sqlite3.connect(f"file:{db.as_posix()}?mode=ro", uri=True, timeout=3.0)
+        try:
+            r = conn.execute("SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name='chat_session'").fetchone()
+            if not r or int(r[0] or 0) == 0:
+                return 0
+            row = conn.execute("SELECT COUNT(*) FROM chat_session").fetchone()
+            return int(row[0] or 0) if row else 0
+        finally:
+            conn.close()
+    except Exception:
+        return None
+
+
+def _checkpoint_wal_best_effort(main: Path) -> None:
+    try:
+        conn = sqlite3.connect(str(main), timeout=5.0)
+        try:
+            conn.execute("PRAGMA wal_checkpoint(TRUNCATE);")
+        finally:
+            conn.close()
+    except Exception:
+        pass
+
+
+def _copy_sqlite_bundle(src_main: Path, dst_main: Path) -> None:
+    dst_main.parent.mkdir(parents=True, exist_ok=True)
+    _checkpoint_wal_best_effort(src_main)
+    for s in _sqlite_sidecars(src_main):
+        if not s.exists():
+            continue
+        d = dst_main.parent / s.name
+        if s.resolve() == d.resolve():
+            continue
+        shutil.copy2(s, d)
+
+
+def _backup_sqlite_bundle_copy(main: Path, dest_dir: Path) -> None:
+    dest_dir.mkdir(parents=True, exist_ok=True)
+    _checkpoint_wal_best_effort(main)
+    for s in _sqlite_sidecars(main):
+        if s.exists():
+            shutil.copy2(s, dest_dir / s.name)
+
+
+def _merge_attachment_tree(src: Path, dst: Path) -> int:
+    if not src.is_dir():
+        return 0
+    dst.mkdir(parents=True, exist_ok=True)
+    copied = 0
+    for f in src.rglob("*"):
+        if not f.is_file():
+            continue
+        rel = f.relative_to(src)
+        target = dst / rel
+        if target.exists():
+            continue
+        target.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(f, target)
+        copied += 1
+    return copied
+
+
+def _run_default_data_migration(canonical_db: Path) -> None:
+    legacy_root_data = _legacy_root_data_root()
+    legacy_root_db = legacy_root_data / "ai_ops.sqlite"
+    if legacy_root_db.exists() and not canonical_db.exists():
+        _copy_sqlite_bundle(legacy_root_db, canonical_db)
+    _merge_attachment_tree(legacy_root_data / "attachments", attachments_dir())
+
+    legacy_platform_data = _legacy_platform_data_root()
+    legacy_db = legacy_platform_data / "ai_ops.sqlite"
+    if legacy_db.exists():
+        leg_n = _sqlite_chat_session_count(legacy_db)
+        if leg_n is not None:
+            if not canonical_db.exists():
+                _copy_sqlite_bundle(legacy_db, canonical_db)
+            else:
+                can_n = _sqlite_chat_session_count(canonical_db)
+                can_n = 0 if can_n is None else can_n
+                force = str(os.getenv("AIA_LEGACY_DB_FORCE_PREMERGE") or os.getenv("OPS_LEGACY_DB_FORCE_PREMERGE") or "").strip().lower() in (
+                    "1",
+                    "true",
+                    "yes",
+                    "on",
+                )
+                if force and leg_n > can_n:
+                    ts = time.strftime("%Y%m%d_%H%M%S")
+                    bak = _canonical_data_root() / f"_pre_merge_sqlite_{ts}"
+                    try:
+                        _checkpoint_wal_best_effort(canonical_db)
+                        _backup_sqlite_bundle_copy(canonical_db, bak)
+                        _copy_sqlite_bundle(legacy_db, canonical_db)
+                    except (OSError, PermissionError, shutil.Error) as exc:
+                        warnings.warn(
+                            "未能用旧库覆盖当前主库（可能被其它进程占用）。"
+                            f"已备份当前库到: {bak}。可关闭占用后重启，或手动合并。原因: {exc}",
+                            RuntimeWarning,
+                            stacklevel=1,
+                        )
+
+    _merge_attachment_tree(legacy_platform_data / "attachments", attachments_dir())
+    _cleanup_pre_merge_backups()
+
+
+def db_path() -> str:
+    global _DATA_MIGRATION_DONE
+    p = os.getenv("AIA_ASSISTANT_DB_PATH") or os.getenv("OPS_ASSISTANT_DB_PATH") or "oclaw/data/ai_ops.sqlite"
+    path = Path(p)
+    if not path.is_absolute():
+        path = (PROJECT_ROOT / path).resolve()
+    use_default = not (os.getenv("AIA_ASSISTANT_DB_PATH") or os.getenv("OPS_ASSISTANT_DB_PATH"))
+    if use_default and not _DATA_MIGRATION_DONE:
+        _DATA_MIGRATION_DONE = True
+        try:
+            _run_default_data_migration(path)
+        except Exception as exc:
+            warnings.warn(f"默认数据目录迁移未完全成功（可稍后重试或检查权限）: {exc}", RuntimeWarning, stacklevel=1)
+    return str(path)
+
+
+__all__ = ["PROJECT_ROOT", "attachments_dir", "db_path"]

platform/embeddings/embedding_client.py

@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+import hashlib
+import os
+from dataclasses import dataclass
+from typing import Any
+
+
+@dataclass(frozen=True)
+class EmbeddingResult:
+    model: str
+    vector: list[float]
+
+
+class EmbeddingClient:
+    def embed(self, text: str) -> EmbeddingResult:
+        raise NotImplementedError
+
+
+class OpenAIEmbeddingClient(EmbeddingClient):
+    def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
+        self.model = (model or os.getenv("OPENAI_EMBEDDING_MODEL") or "text-embedding-3-small").strip()
+        self.api_key = (api_key or os.getenv("OPENAI_API_KEY") or "").strip()
+        self.base_url = (base_url or os.getenv("OPENAI_BASE_URL") or "").strip() or None
+        if not self.api_key:
+            raise RuntimeError("OPENAI_API_KEY is required for embeddings")
+        from openai import OpenAI
+
+        kwargs: dict[str, Any] = {"api_key": self.api_key}
+        if self.base_url:
+            kwargs["base_url"] = self.base_url
+        self._client = OpenAI(**kwargs)
+
+    def embed(self, text: str) -> EmbeddingResult:
+        t = (text or "").strip()
+        if not t:
+            return EmbeddingResult(model=self.model, vector=[0.0] * 8)
+        resp = self._client.embeddings.create(model=self.model, input=t)
+        vec = list(resp.data[0].embedding)
+        return EmbeddingResult(model=self.model, vector=[float(x) for x in vec])
+
+
+class HashEmbeddingClient(EmbeddingClient):
+    """Offline fallback: deterministic small vector (NOT semantic)."""
+
+    def __init__(self, *, dim: int = 32, model: str = "hash-embed-32"):
+        self.dim = int(dim)
+        self.model = model
+
+    def embed(self, text: str) -> EmbeddingResult:
+        t = (text or "").encode("utf-8", errors="ignore")
+        h = hashlib.sha256(t).digest()
+        vec = []
+        for i in range(self.dim):
+            b = h[i % len(h)]
+            vec.append((float(b) / 255.0) * 2.0 - 1.0)
+        return EmbeddingResult(model=self.model, vector=vec)
+
+
+def build_default_embedding_client() -> EmbeddingClient:
+    mode = (os.getenv("AIA_RAG_EMBEDDING_MODE") or "").strip().lower()
+    if mode in ("hash", "offline"):
+        return HashEmbeddingClient()
+    try:
+        return OpenAIEmbeddingClient()
+    except Exception:
+        return HashEmbeddingClient()
+
+
+__all__ = [
+    "EmbeddingClient",
+    "EmbeddingResult",
+    "OpenAIEmbeddingClient",
+    "HashEmbeddingClient",
+    "build_default_embedding_client",
+]
+

platform/files/attachment_assets.py

@@ -0,0 +1,333 @@
+from __future__ import annotations
+
+import base64
+import hashlib
+import json
+import os
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Optional
+
+from oclaw.platform.config.paths import attachments_dir
+_META_SUFFIX: Final[str] = ".meta.json"
+
+
+def _utc_ts() -> int:
+    return int(time.time())
+
+
+def _safe_rel_name(name: str) -> str:
+    # keep it simple: drop path parts, trim, and avoid empty
+    raw = (name or "").replace("\\", "/").split("/")[-1].strip()
+    return raw or "image"
+
+
+def _ext_from_mime(mime: str | None) -> str:
+    m = (mime or "").strip().lower()
+    if m == "image/png":
+        return ".png"
+    if m in ("image/jpg", "image/jpeg"):
+        return ".jpg"
+    if m == "image/webp":
+        return ".webp"
+    if m == "image/gif":
+        return ".gif"
+    return ""
+
+
+def _guess_mime_from_ext(ext: str) -> str:
+    e = (ext or "").lower().lstrip(".")
+    if e == "png":
+        return "image/png"
+    if e in ("jpg", "jpeg"):
+        return "image/jpeg"
+    if e == "webp":
+        return "image/webp"
+    if e == "gif":
+        return "image/gif"
+    return "application/octet-stream"
+
+
+@dataclass(frozen=True)
+class AttachmentMeta:
+    attachment_id: str
+    name: str
+    mime: str
+    bytes: int
+    created_at: int
+    last_access: int
+    width: int | None = None
+    height: int | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        d: dict[str, Any] = {
+            "attachment_id": self.attachment_id,
+            "name": self.name,
+            "mime": self.mime,
+            "bytes": int(self.bytes),
+            "created_at": int(self.created_at),
+            "last_access": int(self.last_access),
+        }
+        if self.width is not None:
+            d["width"] = int(self.width)
+        if self.height is not None:
+            d["height"] = int(self.height)
+        return d
+
+    @staticmethod
+    def from_dict(d: dict[str, Any]) -> "AttachmentMeta":
+        return AttachmentMeta(
+            attachment_id=str(d.get("attachment_id") or ""),
+            name=str(d.get("name") or ""),
+            mime=str(d.get("mime") or "application/octet-stream"),
+            bytes=int(d.get("bytes") or 0),
+            created_at=int(d.get("created_at") or 0),
+            last_access=int(d.get("last_access") or 0),
+            width=int(d["width"]) if d.get("width") is not None else None,
+            height=int(d["height"]) if d.get("height") is not None else None,
+        )
+
+
+class AttachmentAssetStore:
+    """Disk-backed attachment store (project-local by default)."""
+
+    def __init__(self, root_dir: str | Path | None = None):
+        self.root = Path(root_dir) if root_dir is not None else attachments_dir()
+        self.root.mkdir(parents=True, exist_ok=True)
+
+    def _data_path(self, attachment_id: str, *, ext: str) -> Path:
+        # bucket to avoid huge single dir
+        aid = (attachment_id or "").strip()
+        p1, p2 = (aid[:2] or "xx"), (aid[2:4] or "yy")
+        d = self.root / p1 / p2
+        d.mkdir(parents=True, exist_ok=True)
+        return d / f"{aid}{ext}"
+
+    def _meta_path(self, attachment_id: str) -> Path:
+        aid = (attachment_id or "").strip()
+        p1, p2 = (aid[:2] or "xx"), (aid[2:4] or "yy")
+        d = self.root / p1 / p2
+        d.mkdir(parents=True, exist_ok=True)
+        return d / f"{aid}{_META_SUFFIX}"
+
+    def save_bytes(
+        self,
+        data: bytes,
+        *,
+        filename: str,
+        mime: str | None,
+        width: int | None = None,
+        height: int | None = None,
+    ) -> AttachmentMeta:
+        blob = bytes(data or b"")
+        h = hashlib.sha256(blob).hexdigest()
+        ext = _ext_from_mime(mime) or (("." + filename.split(".")[-1].lower()) if "." in filename else "")
+        ext = ext if ext in (".png", ".jpg", ".jpeg", ".webp", ".gif") else _ext_from_mime(mime) or ""
+        data_path = self._data_path(h, ext=ext)
+        meta_path = self._meta_path(h)
+
+        now = _utc_ts()
+        if not data_path.exists():
+            tmp = str(data_path) + ".tmp"
+            with open(tmp, "wb") as f:
+                f.write(blob)
+            os.replace(tmp, data_path)
+
+        meta = AttachmentMeta(
+            attachment_id=h,
+            name=_safe_rel_name(filename),
+            mime=(mime or _guess_mime_from_ext(ext)).strip() or _guess_mime_from_ext(ext),
+            bytes=len(blob),
+            created_at=now,
+            last_access=now,
+            width=width,
+            height=height,
+        )
+        # best-effort: keep existing created_at if present
+        if meta_path.exists():
+            try:
+                prev = json.loads(meta_path.read_text("utf-8"))
+                if isinstance(prev, dict) and prev.get("created_at"):
+                    meta = AttachmentMeta(
+                        **{**meta.to_dict(), "created_at": int(prev.get("created_at") or now)}  # type: ignore[arg-type]
+                    )
+            except Exception:
+                pass
+        meta_path.write_text(json.dumps(meta.to_dict(), ensure_ascii=False), encoding="utf-8")
+        return meta
+
+    def get_meta(self, attachment_id: str) -> Optional[AttachmentMeta]:
+        mp = self._meta_path(attachment_id)
+        if not mp.exists():
+            return None
+        try:
+            obj = json.loads(mp.read_text("utf-8"))
+            if not isinstance(obj, dict):
+                return None
+            return AttachmentMeta.from_dict(obj)
+        except Exception:
+            return None
+
+    def touch(self, attachment_id: str) -> None:
+        mp = self._meta_path(attachment_id)
+        if not mp.exists():
+            return
+        try:
+            obj = json.loads(mp.read_text("utf-8"))
+            if not isinstance(obj, dict):
+                return
+            obj["last_access"] = _utc_ts()
+            mp.write_text(json.dumps(obj, ensure_ascii=False), encoding="utf-8")
+        except Exception:
+            return
+
+    def load_bytes(self, attachment_id: str) -> tuple[bytes, Optional[AttachmentMeta]]:
+        meta = self.get_meta(attachment_id)
+        # try find data file by scanning common extensions
+        exts = (".png", ".jpg", ".jpeg", ".webp", ".gif", "")
+        data_path = None
+        for ext in exts:
+            p = self._data_path(attachment_id, ext=ext)
+            if p.exists():
+                data_path = p
+                break
+        if data_path is None:
+            return b"", meta
+        try:
+            blob = data_path.read_bytes()
+        except Exception:
+            return b"", meta
+        self.touch(attachment_id)
+        return blob, meta
+
+    def get_local_path(self, attachment_id: str) -> Path | None:
+        exts = (".png", ".jpg", ".jpeg", ".webp", ".gif", "")
+        for ext in exts:
+            p = self._data_path(attachment_id, ext=ext)
+            if p.exists():
+                self.touch(attachment_id)
+                return p
+        return None
+
+    def inspect_asset(self, attachment_id: str) -> dict[str, Any]:
+        meta = self.get_meta(attachment_id)
+        p = self.get_local_path(attachment_id)
+        if meta is None and p is None:
+            return {"state": "missing", "attachment_id": attachment_id}
+        if meta is not None and p is None:
+            return {
+                "state": "meta_exists_blob_missing",
+                "attachment_id": attachment_id,
+                "meta": meta.to_dict(),
+            }
+        return {
+            "state": "ok",
+            "attachment_id": attachment_id,
+            "meta": meta.to_dict() if meta else None,
+            "path": str(p) if p is not None else "",
+        }
+
+    def get_render_cache_key(self, attachment_id: str, *, mime_hint: str | None = None) -> tuple[str, str, int, int]:
+        meta = self.get_meta(attachment_id)
+        return (
+            str(attachment_id or "").strip(),
+            str(mime_hint or (meta.mime if meta else "") or "image/jpeg"),
+            int(meta.bytes if meta else 0),
+            int(meta.created_at if meta else 0),
+        )
+
+    def gc(self, *, ttl_days: int = 7) -> dict[str, Any]:
+        ttl_sec = max(1, int(ttl_days)) * 86400
+        now = _utc_ts()
+        removed = 0
+        kept = 0
+
+        if not self.root.exists():
+            return {"ok": True, "removed": 0, "kept": 0}
+
+        for mp in self.root.rglob(f"*{_META_SUFFIX}"):
+            try:
+                obj = json.loads(mp.read_text("utf-8"))
+                if not isinstance(obj, dict):
+                    continue
+                last_access = int(obj.get("last_access") or obj.get("created_at") or 0)
+                aid = str(obj.get("attachment_id") or mp.name.replace(_META_SUFFIX, ""))
+            except Exception:
+                continue
+            if last_access and (now - last_access) <= ttl_sec:
+                kept += 1
+                continue
+
+            # delete meta + possible data files
+            try:
+                mp.unlink(missing_ok=True)  # py3.8+; on 3.14 ok
+            except Exception:
+                pass
+            for ext in (".png", ".jpg", ".jpeg", ".webp", ".gif", ""):
+                try:
+                    self._data_path(aid, ext=ext).unlink(missing_ok=True)
+                except Exception:
+                    pass
+            removed += 1
+
+        return {"ok": True, "removed": removed, "kept": kept}
+
+
+_LAST_GC_TS: int = 0
+
+
+def gc_if_due(*, ttl_days: int = 7, min_interval_sec: int = 3600, root_dir: str | Path | None = None) -> None:
+    """Best-effort throttled GC to be called in UI loop."""
+    global _LAST_GC_TS
+    now = _utc_ts()
+    if _LAST_GC_TS and (now - _LAST_GC_TS) < max(10, int(min_interval_sec)):
+        return
+    _LAST_GC_TS = now
+    try:
+        AttachmentAssetStore(root_dir).gc(ttl_days=ttl_days)
+    except Exception:
+        return
+
+
+def image_ref_to_data_url(att: dict[str, Any], *, root_dir: str | Path | None = None) -> tuple[str, str]:
+    """Helper for UI: convert image_ref to (mime, data_url_base64_payload)."""
+    aid = str(att.get("attachment_id") or "").strip()
+    if not aid:
+        return "image/jpeg", ""
+    store = AttachmentAssetStore(root_dir)
+    blob, meta = store.load_bytes(aid)
+    mime = str(att.get("mime") or (meta.mime if meta else "image/jpeg") or "image/jpeg")
+    if not blob:
+        return mime, ""
+    b64 = base64.b64encode(blob).decode("ascii")
+    return mime, b64
+
+
+def attachment_id_to_data_url(
+    attachment_id: str,
+    *,
+    mime: str | None = None,
+    root_dir: str | Path | None = None,
+) -> str:
+    """Convert stored attachment bytes into data URL form."""
+    aid = str(attachment_id or "").strip()
+    if not aid:
+        return ""
+    store = AttachmentAssetStore(root_dir)
+    blob, meta = store.load_bytes(aid)
+    if not blob:
+        return ""
+    resolved_mime = str(mime or (meta.mime if meta else "") or "image/jpeg")
+    b64 = base64.b64encode(blob).decode("ascii")
+    return f"data:{resolved_mime};base64,{b64}"
+
+
+__all__ = [
+    "AttachmentAssetStore",
+    "AttachmentMeta",
+    "attachment_id_to_data_url",
+    "gc_if_due",
+    "image_ref_to_data_url",
+]
+

platform/files/file_attachments.py

@@ -0,0 +1,479 @@
+from __future__ import annotations
+
+"""将上传文件解析为附件字典（不依赖 Streamlit）。"""
+
+import io
+import json
+import os
+import re
+import zipfile
+from functools import lru_cache
+from pathlib import Path
+from pathlib import PurePosixPath
+from typing import Any
+
+import pandas as pd
+from docx import Document
+from PIL import Image
+from PyPDF2 import PdfReader
+
+from oclaw.platform.config.paths import PROJECT_ROOT
+from oclaw.platform.files.attachment_assets import AttachmentAssetStore
+from oclaw.platform.files.tabular_attachment_store import save_dataframe, save_workbook
+
+
+def _sniff_image_mime(data: bytes) -> str | None:
+    if len(data) < 12:
+        return None
+    if data[:8] == b"\x89PNG\r\n\x1a\n":
+        return "image/png"
+    if data[:3] == b"\xff\xd8\xff":
+        return "image/jpeg"
+    if data[:6] in (b"GIF87a", b"GIF89a"):
+        return "image/gif"
+    if len(data) >= 12 and data[:4] == b"RIFF" and data[8:12] == b"WEBP":
+        return "image/webp"
+    return None
+
+
+MAX_TEXT_CHARS = 12_000
+EXCEL_PREVIEW_ROWS = 80
+LARGE_TABLE_PREVIEW_ROWS = 20
+MAX_ZIP_DEPTH = 2
+MAX_ZIP_FILE_COUNT = 200
+MAX_ZIP_ENTRY_BYTES = 10 * 1024 * 1024
+MAX_ZIP_TOTAL_UNCOMPRESSED_BYTES = 50 * 1024 * 1024
+DEFAULT_TABULAR_ROWS_READ = 5000
+DEFAULT_TABULAR_COLUMNS = 200
+DEFAULT_TABULAR_CELL_CHARS = 500
+DEFAULT_TABULAR_TOOL_MODE_ENABLED = True
+DEFAULT_TABULAR_TOOL_MODE_MIN_ROWS = 20_000
+DEFAULT_TABULAR_TOOL_MODE_MAX_BYTES = 30 * 1024 * 1024
+DEFAULT_LARGE_TABLE_PREVIEW_ROWS = LARGE_TABLE_PREVIEW_ROWS
+MAX_ZIP_MEMBER_NAME_LENGTH = 240
+MAX_EXCEL_ZIP_FILE_COUNT = 500
+MAX_EXCEL_ZIP_ENTRY_BYTES = 20 * 1024 * 1024
+MAX_EXCEL_ZIP_TOTAL_UNCOMPRESSED_BYTES = 120 * 1024 * 1024
+DEFAULT_MAX_EXCEL_SHEETS = 50
+
+
+def _truncate_text(text: str, limit: int = MAX_TEXT_CHARS) -> str:
+    t = str(text or "")
+    if len(t) <= limit:
+        return t
+    return t[:limit] + f"\n\n...[truncated to {limit} chars]"
+
+
+def _decode_text_bytes(data: bytes) -> str:
+    try:
+        return data.decode("utf-8")
+    except UnicodeDecodeError:
+        try:
+            return data.decode("gbk")
+        except Exception:
+            return "Error decoding text file (unknown encoding)"
+
+
+def _clean_html_to_text(raw: str) -> str:
+    text = re.sub(r"(?is)<script[^>]*>.*?</script>", " ", str(raw or ""))
+    text = re.sub(r"(?is)<style[^>]*>.*?</style>", " ", text)
+    text = re.sub(r"(?is)<[^>]+>", " ", text)
+    text = re.sub(r"[ \t\r\f\v]+", " ", text)
+    text = re.sub(r"\n{3,}", "\n\n", text)
+    return text.strip()
+
+
+def _safe_int(raw: Any, default: int, *, min_value: int = 1, max_value: int = 2_000_000) -> int:
+    try:
+        value = int(raw)
+    except Exception:
+        return default
+    if value < min_value:
+        return default
+    return min(value, max_value)
+
+
+@lru_cache(maxsize=1)
+def _attachments_limits() -> dict[str, Any]:
+    cfg_path_raw = str(os.getenv("AIA_OCLAW_CONFIG_PATH") or "").strip()
+    cfg_path = Path(cfg_path_raw).expanduser() if cfg_path_raw else (Path(PROJECT_ROOT) / "oclaw" / "oclaw.json").resolve()
+    if not cfg_path.is_absolute():
+        cfg_path = (Path(PROJECT_ROOT) / cfg_path).resolve()
+    tabular_cfg: dict[str, Any] = {}
+    try:
+        obj = json.loads(cfg_path.read_text(encoding="utf-8"))
+        tabular_cfg = (
+            (((obj or {}).get("plugins") or {}).get("entries") or {}).get("memory-wiki", {}).get("auto", {}).get("attachments", {}).get("tabular", {}) or {}
+        )
+    except Exception:
+        tabular_cfg = {}
+    return {
+        "rows_read": _safe_int(tabular_cfg.get("max_rows_read"), DEFAULT_TABULAR_ROWS_READ),
+        "columns": _safe_int(tabular_cfg.get("max_columns"), DEFAULT_TABULAR_COLUMNS),
+        "cell_chars": _safe_int(tabular_cfg.get("max_cell_chars"), DEFAULT_TABULAR_CELL_CHARS),
+        "max_excel_sheets": _safe_int(tabular_cfg.get("max_excel_sheets"), DEFAULT_MAX_EXCEL_SHEETS, max_value=500),
+        "large_table_preview_rows": _safe_int(
+            tabular_cfg.get("large_table_preview_rows"), DEFAULT_LARGE_TABLE_PREVIEW_ROWS, max_value=500
+        ),
+        "tool_mode_enabled": bool(tabular_cfg.get("tool_mode_enabled", DEFAULT_TABULAR_TOOL_MODE_ENABLED)),
+        "tool_mode_min_rows": _safe_int(tabular_cfg.get("tool_mode_min_rows"), DEFAULT_TABULAR_TOOL_MODE_MIN_ROWS),
+        "tool_mode_max_bytes": _safe_int(
+            tabular_cfg.get("tool_mode_max_bytes"), DEFAULT_TABULAR_TOOL_MODE_MAX_BYTES, max_value=500 * 1024 * 1024
+        ),
+    }
+
+
+def clear_attachment_limits_cache() -> None:
+    _attachments_limits.cache_clear()
+
+
+def _clip_tabular_cells(df: pd.DataFrame, *, max_cell_chars: int) -> tuple[pd.DataFrame, bool]:
+    clipped_any = False
+
+    def _clip_cell(value: Any) -> Any:
+        nonlocal clipped_any
+        if value is None:
+            return value
+        s = str(value)
+        if len(s) <= max_cell_chars:
+            return value
+        clipped_any = True
+        return s[:max_cell_chars] + "...[cell-truncated]"
+
+    out = df.copy()
+    for col in out.columns:
+        out[col] = out[col].map(_clip_cell)
+    return out, clipped_any
+
+
+def _dataframe_summary_text(
+    df: pd.DataFrame,
+    *,
+    sampled: bool = False,
+    clipped_columns: bool = False,
+    clipped_cells: bool = False,
+    preview_rows: int = EXCEL_PREVIEW_ROWS,
+) -> str:
+    rows = int(len(df.index))
+    cols = int(len(df.columns))
+    header = [str(x) for x in list(df.columns)]
+    dtypes = [f"{str(k)}:{str(v)}" for k, v in df.dtypes.items()]
+    show_rows = max(1, int(preview_rows or EXCEL_PREVIEW_ROWS))
+    sample = df.head(show_rows).to_string(index=False)
+    body = (
+        f"# Table Summary\n"
+        f"- rows: {rows}\n"
+        f"- cols: {cols}\n"
+        f"- sampled: {'yes' if sampled else 'no'}\n"
+        f"- clipped_columns: {'yes' if clipped_columns else 'no'}\n"
+        f"- clipped_cells: {'yes' if clipped_cells else 'no'}\n"
+        f"- columns: {', '.join(header)}\n"
+        f"- dtypes: {', '.join(dtypes)}\n\n"
+        f"## Preview (first {min(rows, show_rows)} rows)\n"
+        f"{sample}"
+    )
+    return _truncate_text(body)
+
+
+def _pdf_summary_text(reader: PdfReader) -> str:
+    pages = list(getattr(reader, "pages", []) or [])
+    chunks: list[str] = [f"# PDF Summary\n- pages: {len(pages)}\n"]
+    for idx, page in enumerate(pages, start=1):
+        txt = ""
+        try:
+            txt = str(page.extract_text() or "").strip()
+        except Exception:
+            txt = ""
+        if not txt:
+            txt = "(empty page text)"
+        chunks.append(f"## Page {idx}\n{txt}")
+    return _truncate_text("\n\n".join(chunks))
+
+
+def _is_safe_zip_member_name(name: str) -> bool:
+    n = str(name or "").replace("\\", "/").strip()
+    if not n:
+        return False
+    if len(n) > MAX_ZIP_MEMBER_NAME_LENGTH:
+        return False
+    if any(ord(ch) < 32 for ch in n):
+        return False
+    p = PurePosixPath(n)
+    if p.is_absolute():
+        return False
+    if any(part in ("..", "") for part in p.parts):
+        return False
+    return True
+
+
+def _validate_excel_zip_payload(data: bytes) -> str | None:
+    try:
+        with zipfile.ZipFile(io.BytesIO(data)) as zf:
+            infos = [x for x in zf.infolist() if not x.is_dir()]
+            if len(infos) > MAX_EXCEL_ZIP_FILE_COUNT:
+                return f"excel zip has too many entries ({len(infos)} > {MAX_EXCEL_ZIP_FILE_COUNT})"
+            total_uncompressed = 0
+            for info in infos:
+                if not _is_safe_zip_member_name(info.filename):
+                    return f"excel zip contains unsafe path: {info.filename}"
+                fsize = int(info.file_size or 0)
+                total_uncompressed += fsize
+                if fsize > MAX_EXCEL_ZIP_ENTRY_BYTES:
+                    return f"excel zip entry too large: {info.filename} ({fsize} > {MAX_EXCEL_ZIP_ENTRY_BYTES})"
+                if total_uncompressed > MAX_EXCEL_ZIP_TOTAL_UNCOMPRESSED_BYTES:
+                    return (
+                        "excel zip total uncompressed size too large "
+                        f"({total_uncompressed} > {MAX_EXCEL_ZIP_TOTAL_UNCOMPRESSED_BYTES})"
+                    )
+        return None
+    except Exception:
+        return "invalid excel zip payload"
+
+
+def process_zip(zip_data: bytes, *, _depth: int = 0) -> list[dict[str, Any]]:
+    if _depth > MAX_ZIP_DEPTH:
+        return [
+            {
+                "type": "text",
+                "name": "zip-error",
+                "content": f"ZIP nesting too deep (>{MAX_ZIP_DEPTH})",
+            }
+        ]
+    attachments: list[dict[str, Any]] = []
+    try:
+        with zipfile.ZipFile(io.BytesIO(zip_data)) as zf:
+            infos = [x for x in zf.infolist() if not x.is_dir()]
+            if len(infos) > MAX_ZIP_FILE_COUNT:
+                raise ValueError(f"ZIP has too many files ({len(infos)} > {MAX_ZIP_FILE_COUNT})")
+            total_uncompressed = 0
+            for info in infos:
+                if not _is_safe_zip_member_name(info.filename):
+                    raise ValueError(f"ZIP contains unsafe path: {info.filename}")
+                total_uncompressed += int(info.file_size or 0)
+                if int(info.file_size or 0) > MAX_ZIP_ENTRY_BYTES:
+                    raise ValueError(
+                        f"ZIP entry too large: {info.filename} ({int(info.file_size or 0)} > {MAX_ZIP_ENTRY_BYTES})"
+                    )
+                if total_uncompressed > MAX_ZIP_TOTAL_UNCOMPRESSED_BYTES:
+                    raise ValueError(
+                        "ZIP total uncompressed size too large "
+                        f"({total_uncompressed} > {MAX_ZIP_TOTAL_UNCOMPRESSED_BYTES})"
+                    )
+                with zf.open(info) as f:
+                    data = f.read()
+                    att = process_file_data(info.filename, data, _zip_depth=_depth + 1)
+                    if att:
+                        attachments.extend(att)
+    except Exception as e:
+        attachments.append(
+            {
+                "type": "text",
+                "name": "zip-error",
+                "content": f"Error parsing zip: {e}",
+            }
+        )
+    return attachments
+
+
+def process_file_data(name: str, data: bytes, *, _zip_depth: int = 0) -> list[dict[str, Any]]:
+    ext = name.split(".")[-1].lower() if "." in name else ""
+    attachments: list[dict[str, Any]] = []
+
+    if ext in ("png", "jpg", "jpeg", "webp", "gif", "jfif", "pjpeg", "bmp", "tif", "tiff"):
+        if ext == "png":
+            mime = "image/png"
+        elif ext in ("jpg", "jpeg", "jfif", "pjpeg"):
+            mime = "image/jpeg"
+        elif ext == "webp":
+            mime = "image/webp"
+        elif ext == "gif":
+            mime = "image/gif"
+        elif ext == "bmp":
+            mime = "image/bmp"
+        elif ext in ("tif", "tiff"):
+            mime = "image/tiff"
+        width = None
+        height = None
+        try:
+            with Image.open(io.BytesIO(data)) as im:
+                width, height = im.size
+        except Exception:
+            pass
+        meta = AttachmentAssetStore().save_bytes(data, filename=name, mime=mime, width=width, height=height)
+        attachments.append(
+            {
+                "type": "image_ref",
+                "name": meta.name,
+                "mime": meta.mime,
+                "attachment_id": meta.attachment_id,
+                "bytes": meta.bytes,
+                "width": meta.width,
+                "height": meta.height,
+            }
+        )
+        return attachments
+
+    sniff_mime = _sniff_image_mime(data)
+    if sniff_mime:
+        width = None
+        height = None
+        try:
+            with Image.open(io.BytesIO(data)) as im:
+                width, height = im.size
+        except Exception:
+            pass
+        meta = AttachmentAssetStore().save_bytes(data, filename=name, mime=sniff_mime, width=width, height=height)
+        attachments.append(
+            {
+                "type": "image_ref",
+                "name": meta.name,
+                "mime": meta.mime,
+                "attachment_id": meta.attachment_id,
+                "bytes": meta.bytes,
+                "width": meta.width,
+                "height": meta.height,
+            }
+        )
+        return attachments
+
+    if ext in ("xlsx", "xls", "csv"):
+        try:
+            if ext == "xlsx":
+                zip_err = _validate_excel_zip_payload(data)
+                if zip_err:
+                    attachments.append({"type": "text", "name": name, "content": _truncate_text(f"Error parsing excel/csv: {zip_err}")})
+                    return attachments
+            limits = _attachments_limits()
+            rows_read = int(limits.get("rows_read") or DEFAULT_TABULAR_ROWS_READ)
+            max_columns = int(limits.get("columns") or DEFAULT_TABULAR_COLUMNS)
+            max_cell_chars = int(limits.get("cell_chars") or DEFAULT_TABULAR_CELL_CHARS)
+            max_excel_sheets = int(limits.get("max_excel_sheets") or DEFAULT_MAX_EXCEL_SHEETS)
+            large_table_preview_rows = int(
+                limits.get("large_table_preview_rows") or DEFAULT_LARGE_TABLE_PREVIEW_ROWS
+            )
+            tool_mode_enabled = bool(limits.get("tool_mode_enabled", DEFAULT_TABULAR_TOOL_MODE_ENABLED))
+            tool_mode_min_rows = int(limits.get("tool_mode_min_rows") or DEFAULT_TABULAR_TOOL_MODE_MIN_ROWS)
+            tool_mode_max_bytes = int(limits.get("tool_mode_max_bytes") or DEFAULT_TABULAR_TOOL_MODE_MAX_BYTES)
+            if ext == "csv":
+                df = pd.read_csv(io.BytesIO(data), nrows=rows_read)
+            else:
+                df = pd.read_excel(io.BytesIO(data), nrows=rows_read)
+            sampled = int(len(df.index)) >= rows_read
+            estimated_rows = 0
+            if ext == "csv":
+                estimated_rows = max(0, int(data.count(b"\n")) - 1)
+            else:
+                estimated_rows = int(len(df.index))
+            clipped_columns = int(len(df.columns)) > max_columns
+            if clipped_columns:
+                df = df.iloc[:, :max_columns]
+            df, clipped_cells = _clip_tabular_cells(df, max_cell_chars=max_cell_chars)
+            tool_mode = bool(
+                tool_mode_enabled
+                and int(len(data or b"")) <= tool_mode_max_bytes
+                and (estimated_rows >= tool_mode_min_rows or (ext in ("xlsx", "xls") and sampled))
+            )
+            attachments.append(
+                {
+                    "type": "text",
+                    "name": name,
+                    "content": _dataframe_summary_text(
+                        df,
+                        sampled=sampled,
+                        clipped_columns=clipped_columns,
+                        clipped_cells=clipped_cells,
+                        preview_rows=(large_table_preview_rows if tool_mode else EXCEL_PREVIEW_ROWS),
+                    )
+                    + (
+                        (
+                            f"\n\n# Large Table Mode\n- estimated_rows: {estimated_rows}\n- query_tool: query_tabular_attachment\n"
+                            "- note: use the `table_id` from the accompanying `tabular_ref` attachment to query full data."
+                        )
+                        if tool_mode
+                        else ""
+                    ),
+                }
+            )
+            if tool_mode:
+                workbook_sheets: dict[str, pd.DataFrame]
+                clipped_sheets = False
+                if ext == "csv":
+                    full_df = pd.read_csv(io.BytesIO(data), dtype=str)
+                    if int(len(full_df.columns)) > max_columns:
+                        full_df = full_df.iloc[:, :max_columns]
+                    full_df, _ = _clip_tabular_cells(full_df, max_cell_chars=max_cell_chars)
+                    workbook_sheets = {"Sheet1": full_df}
+                else:
+                    excel_obj = pd.read_excel(io.BytesIO(data), dtype=str, sheet_name=None)
+                    workbook_sheets = {}
+                    sheet_items = list((excel_obj or {}).items())
+                    if len(sheet_items) > max_excel_sheets:
+                        clipped_sheets = True
+                        sheet_items = sheet_items[:max_excel_sheets]
+                    for sheet_name, sheet_df in sheet_items:
+                        sdf = sheet_df
+                        if int(len(sdf.columns)) > max_columns:
+                            sdf = sdf.iloc[:, :max_columns]
+                        sdf, _ = _clip_tabular_cells(sdf, max_cell_chars=max_cell_chars)
+                        workbook_sheets[str(sheet_name or f"Sheet{len(workbook_sheets)+1}")] = sdf
+                mime = "text/csv" if ext == "csv" else "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                meta = AttachmentAssetStore().save_bytes(data, filename=name, mime=mime)
+                table_meta = (
+                    save_dataframe(attachment_id=str(meta.attachment_id), name=name, df=workbook_sheets.get("Sheet1", pd.DataFrame()))
+                    if ext == "csv"
+                    else save_workbook(attachment_id=str(meta.attachment_id), name=name, sheets=workbook_sheets)
+                )
+                attachments.append(
+                    {
+                        "type": "tabular_ref",
+                        "name": str(name),
+                        "attachment_id": str(meta.attachment_id),
+                        "table_id": str(table_meta.get("table_id") or ""),
+                        "rows": int(table_meta.get("rows") or 0),
+                        "cols": int(table_meta.get("cols") or 0),
+                        "columns": list(table_meta.get("columns") or []),
+                        "sheets": list(table_meta.get("sheets") or []),
+                    }
+                )
+                if clipped_sheets:
+                    attachments.append(
+                        {
+                            "type": "text",
+                            "name": f"{name}.sheet-limit",
+                            "content": (
+                                f"Excel workbook contains too many sheets; only the first {max_excel_sheets} sheets were stored "
+                                "for tool-mode querying. Adjust max_excel_sheets if full coverage is required."
+                            ),
+                        }
+                    )
+        except Exception as e:
+            attachments.append({"type": "text", "name": name, "content": _truncate_text(f"Error parsing excel/csv: {e}")})
+
+    elif ext == "docx":
+        try:
+            doc = Document(io.BytesIO(data))
+            text = "\n".join([p.text for p in doc.paragraphs])
+            attachments.append({"type": "text", "name": name, "content": _truncate_text(text)})
+        except Exception as e:
+            attachments.append({"type": "text", "name": name, "content": _truncate_text(f"Error parsing docx: {e}")})
+
+    elif ext == "pdf":
+        try:
+            reader = PdfReader(io.BytesIO(data))
+            attachments.append({"type": "text", "name": name, "content": _pdf_summary_text(reader)})
+        except Exception as e:
+            attachments.append({"type": "text", "name": name, "content": _truncate_text(f"Error parsing pdf: {e}")})
+
+    elif ext in ("txt", "py", "js", "html", "css", "md", "json", "yaml", "yml", "sh", "log"):
+        text = _decode_text_bytes(data)
+        if ext == "html":
+            text = _clean_html_to_text(text)
+        attachments.append({"type": "text", "name": name, "content": _truncate_text(text)})
+
+    elif ext == "zip":
+        attachments.extend(process_zip(data, _depth=_zip_depth))
+
+    return attachments
+
+
+__all__ = ["process_zip", "process_file_data", "clear_attachment_limits_cache"]

platform/files/session_export.py

@@ -0,0 +1,66 @@
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from oclaw.platform.persistence.sqlite_store import ChatMessage, SqliteStore
+
+
+def export_session_json(store: SqliteStore, session_id: str, message_limit: int = 50_000) -> str:
+    title = ""
+    s = store.get_session(session_id)
+    if s:
+        title = s.title
+    msgs = store.get_messages(session_id=session_id, limit=message_limit)
+    payload: dict[str, Any] = {
+        "session_id": session_id,
+        "title": title,
+        "messages": [_message_to_dict(m) for m in msgs],
+    }
+    return json.dumps(payload, ensure_ascii=False, indent=2)
+
+
+def export_session_markdown(store: SqliteStore, session_id: str, message_limit: int = 50_000) -> str:
+    s = store.get_session(session_id)
+    title = (s.title if s else "") or session_id[:12]
+    lines = [f"# {title}", "", f"Session: `{session_id}`", ""]
+    for m in store.get_messages(session_id=session_id, limit=message_limit):
+        lines.extend(_message_to_markdown_lines(m))
+        lines.append("")
+    return "\n".join(lines).rstrip() + "\n"
+
+
+def _message_to_dict(m: ChatMessage) -> dict[str, Any]:
+    return {
+        "id": m.id,
+        "role": m.role,
+        "content": m.content,
+        "tool_calls": m.tool_calls,
+        "attachments": m.attachments,
+        "timestamp": m.timestamp,
+    }
+
+
+def _message_to_markdown_lines(m: ChatMessage) -> list[str]:
+    role = m.role.upper()
+    ts = m.timestamp or ""
+    head = f"## {role} · `{m.id}` · {ts}"
+    body = m.content or ""
+    if m.role == "tool" and m.tool_calls:
+        head = f"## TOOL · `{m.id}` · {ts}"
+    lines = [head, ""]
+    if body.strip():
+        lines.append(body)
+        lines.append("")
+    if m.tool_calls and m.role != "tool":
+        lines.append("```json")
+        lines.append(m.tool_calls)
+        lines.append("```")
+    elif m.tool_calls and m.role == "tool":
+        lines.append(f"_meta_: `{m.tool_calls}`")
+    if m.attachments:
+        lines.append("_attachments_: see JSON export for full data.")
+    return lines
+
+
+__all__ = ["export_session_json", "export_session_markdown"]

platform/files/tabular_attachment_store.py

@@ -0,0 +1,659 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import re
+import sqlite3
+import subprocess
+import time
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from oclaw.platform.config.paths import PROJECT_ROOT
+from oclaw.platform.config.paths import attachments_dir
+
+
+def _tabular_root() -> Path:
+    p = (attachments_dir() / "tabular").resolve()
+    p.mkdir(parents=True, exist_ok=True)
+    return p
+
+
+def _table_id(attachment_id: str, name: str) -> str:
+    raw = f"{attachment_id}:{name}".encode("utf-8", errors="ignore")
+    return hashlib.sha256(raw).hexdigest()
+
+
+def _db_path(table_id: str) -> Path:
+    return _tabular_root() / f"{table_id}.sqlite"
+
+
+def _meta_path(table_id: str) -> Path:
+    return _tabular_root() / f"{table_id}.meta.json"
+
+
+def _normalize_columns(raw_cols: list[Any]) -> tuple[list[str], list[dict[str, Any]]]:
+    out: list[str] = []
+    mapping: list[dict[str, Any]] = []
+    seen: dict[str, int] = {}
+    for idx, raw in enumerate(raw_cols):
+        original = "" if raw is None else str(raw)
+        base = original.strip()
+        if not base or base.lower().startswith("unnamed:"):
+            base = f"col_{idx + 1}"
+        base = re.sub(r"\s+", "_", base)
+        base = re.sub(r"\W+", "_", base, flags=re.UNICODE).strip("_")
+        if not base:
+            base = f"col_{idx + 1}"
+        n = int(seen.get(base, 0)) + 1
+        seen[base] = n
+        name = base if n == 1 else f"{base}__{n}"
+        out.append(name)
+        mapping.append({"index": idx, "original": original, "normalized": name})
+    return out, mapping
+
+
+def _prepare_writable_df(df: pd.DataFrame) -> tuple[pd.DataFrame, list[str], list[dict[str, Any]]]:
+    raw_cols = [x for x in list(df.columns)]
+    cols, col_map = _normalize_columns(raw_cols)
+    writable = df.copy()
+    writable.columns = cols
+    for c in cols:
+        writable[c] = writable[c].map(lambda x: "" if x is None else str(x))
+    return writable, cols, col_map
+
+
+def _safe_sheet_table_name(sheet_name: str, idx: int) -> str:
+    base = re.sub(r"[^0-9a-zA-Z_]+", "_", str(sheet_name or "").strip()).strip("_").lower()
+    if not base:
+        base = f"sheet_{idx + 1}"
+    return f"sheet_{idx + 1}_{base}"
+
+
+def _mcp_sqlite_command() -> list[str]:
+    raw = str(os.getenv("AIA_MCP_SQLITE_COMMAND") or "").strip()
+    if not raw:
+        return []
+    return [x for x in raw.split(" ") if x]
+
+
+def _tabular_use_mcp() -> bool:
+    raw = str(os.getenv("AIA_TABULAR_USE_MCP") or "").strip().lower()
+    return raw in {"1", "true", "yes", "on"}
+
+
+def _tabular_sql_timeout_ms() -> int:
+    def _clamp_timeout(raw: Any, default: int = 8_000) -> int:
+        try:
+            n = int(raw)
+        except Exception:
+            return default
+        return max(100, min(n, 120_000))
+
+    raw = str(os.getenv("AIA_TABULAR_SQL_TIMEOUT_MS") or "").strip()
+    if raw:
+        return _clamp_timeout(raw)
+    cfg_path_raw = str(os.getenv("AIA_OCLAW_CONFIG_PATH") or "").strip()
+    cfg_path = Path(cfg_path_raw).expanduser() if cfg_path_raw else (Path(PROJECT_ROOT) / "oclaw" / "oclaw.json").resolve()
+    if not cfg_path.is_absolute():
+        cfg_path = (Path(PROJECT_ROOT) / cfg_path).resolve()
+    try:
+        obj = json.loads(cfg_path.read_text(encoding="utf-8"))
+    except Exception:
+        return 8_000
+    tabular_cfg = (
+        (((obj or {}).get("plugins") or {}).get("entries") or {}).get("memory-wiki", {}).get("auto", {}).get("attachments", {}).get("tabular", {}) or {}
+    )
+    if not isinstance(tabular_cfg, dict):
+        return 8_000
+    return _clamp_timeout(tabular_cfg.get("sql_timeout_ms"), 8_000)
+
+
+def _extract_rows_from_mcp_result(res: dict[str, Any]) -> list[dict[str, Any]] | None:
+    if not isinstance(res, dict) or not bool(res.get("ok")):
+        return None
+    data = res.get("data") if isinstance(res.get("data"), dict) else res.get("result")
+    if isinstance(data, dict):
+        direct_rows = data.get("rows")
+        if isinstance(direct_rows, list):
+            out: list[dict[str, Any]] = []
+            for r in direct_rows:
+                if isinstance(r, dict):
+                    out.append({str(k): r[k] for k in r.keys()})
+            return out
+        content = data.get("content")
+        if isinstance(content, list):
+            for it in content:
+                if not isinstance(it, dict):
+                    continue
+                txt = str(it.get("text") or "").strip()
+                if not txt:
+                    continue
+                try:
+                    obj = json.loads(txt)
+                except Exception:
+                    continue
+                if isinstance(obj, dict) and isinstance(obj.get("rows"), list):
+                    rows = obj.get("rows")
+                    out: list[dict[str, Any]] = []
+                    for r in rows:
+                        if isinstance(r, dict):
+                            out.append({str(k): r[k] for k in r.keys()})
+                    return out
+    return None
+
+
+def _run_select_via_mcp(*, db_path: Path, sql: str, params: list[Any]) -> list[dict[str, Any]] | None:
+    if not _tabular_use_mcp():
+        return None
+    cmd = _mcp_sqlite_command()
+    if not cmd:
+        return None
+
+    def _call_once(tool_name: str, arguments: dict[str, Any]) -> dict[str, Any]:
+        try:
+            p = subprocess.Popen(
+                cmd,
+                stdin=subprocess.PIPE,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                text=True,
+                encoding="utf-8",
+            )
+        except Exception:
+            return {"ok": False}
+        try:
+            if p.stdin is None or p.stdout is None:
+                return {"ok": False}
+            init_req = {
+                "jsonrpc": "2.0",
+                "id": 1,
+                "method": "initialize",
+                "params": {
+                    "protocolVersion": "2024-11-05",
+                    "capabilities": {},
+                    "clientInfo": {"name": "oclaw-tabular", "version": "0.1.0"},
+                },
+            }
+            p.stdin.write(json.dumps(init_req, ensure_ascii=False) + "\n")
+            p.stdin.flush()
+            _ = p.stdout.readline()
+            p.stdin.write(json.dumps({"jsonrpc": "2.0", "method": "notifications/initialized", "params": {}}, ensure_ascii=False) + "\n")
+            p.stdin.flush()
+            call_req = {
+                "jsonrpc": "2.0",
+                "id": 2,
+                "method": "tools/call",
+                "params": {"name": str(tool_name), "arguments": arguments},
+            }
+            p.stdin.write(json.dumps(call_req, ensure_ascii=False) + "\n")
+            p.stdin.flush()
+            line = p.stdout.readline()
+            if not line:
+                return {"ok": False}
+            obj = json.loads(line)
+            if not isinstance(obj, dict):
+                return {"ok": False}
+            if isinstance(obj.get("error"), dict):
+                return {"ok": False}
+            return {"ok": True, "result": obj.get("result")}
+        except Exception:
+            return {"ok": False}
+        finally:
+            try:
+                p.terminate()
+            except Exception:
+                pass
+
+    # Probe several common sqlite MCP tool names/argument shapes.
+    attempts: list[tuple[str, dict[str, Any]]] = [
+        ("query", {"database": str(db_path), "sql": sql, "params": params}),
+        ("query", {"db_path": str(db_path), "sql": sql, "params": params}),
+        ("execute_sql", {"database": str(db_path), "sql": sql, "params": params}),
+        ("read_query", {"database": str(db_path), "query": sql, "params": params}),
+        ("sqlite_query", {"database": str(db_path), "sql": sql, "params": params}),
+    ]
+    for tool_name, args in attempts:
+        res = _call_once(tool_name=tool_name, arguments=args)
+        rows = _extract_rows_from_mcp_result(res if isinstance(res, dict) else {})
+        if rows is not None:
+            return rows
+    return None
+
+
+def _run_select_sql(
+    *,
+    db_path: Path,
+    sql: str,
+    params: list[Any],
+    timeout_ms: int | None = None,
+) -> tuple[list[dict[str, Any]], str]:
+    rows = _run_select_via_mcp(db_path=db_path, sql=sql, params=params)
+    if rows is not None:
+        return rows, "mcp_sqlite"
+    out_rows: list[dict[str, Any]] = []
+    timeout_ms_eff = max(100, int(timeout_ms or _tabular_sql_timeout_ms()))
+    deadline = time.perf_counter() + (float(timeout_ms_eff) / 1000.0)
+    timed_out = {"hit": False}
+    with sqlite3.connect(str(db_path)) as conn:
+        conn.row_factory = sqlite3.Row
+
+        def _progress_cb() -> int:
+            if time.perf_counter() > deadline:
+                timed_out["hit"] = True
+                return 1
+            return 0
+
+        conn.set_progress_handler(_progress_cb, 1000)
+        try:
+            for r in conn.execute(sql, params).fetchall():
+                out_rows.append({k: r[k] for k in r.keys()})
+        except sqlite3.OperationalError as exc:
+            if timed_out["hit"] and "interrupted" in str(exc).lower():
+                raise TimeoutError(f"tabular_sql_timeout_ms={timeout_ms_eff}") from exc
+            raise
+        finally:
+            conn.set_progress_handler(None, 0)
+    return out_rows, "builtin_sqlite"
+
+
+def save_dataframe(*, attachment_id: str, name: str, df: pd.DataFrame) -> dict[str, Any]:
+    return save_workbook(attachment_id=attachment_id, name=name, sheets={"Sheet1": df})
+
+
+def save_workbook(*, attachment_id: str, name: str, sheets: dict[str, pd.DataFrame]) -> dict[str, Any]:
+    tid = _table_id(attachment_id=attachment_id, name=name)
+    db = _db_path(tid)
+    meta = _meta_path(tid)
+    sheet_rows: list[dict[str, Any]] = []
+    default_table = "rows_data"
+    default_columns: list[str] = []
+    default_rows = 0
+    default_cols = 0
+    with sqlite3.connect(str(db)) as conn:
+        for idx, (sheet_name, df) in enumerate((sheets or {}).items()):
+            writable, cols, col_map = _prepare_writable_df(df)
+            table_name = _safe_sheet_table_name(str(sheet_name or f"Sheet{idx + 1}"), idx)
+            writable.to_sql(table_name, conn, if_exists="replace", index=False)
+            if idx == 0:
+                # backward compatibility: old queries use rows_data
+                writable.to_sql("rows_data", conn, if_exists="replace", index=False)
+                default_table = table_name
+                default_columns = list(cols)
+                default_rows = int(len(writable.index))
+                default_cols = int(len(cols))
+            sheet_rows.append(
+                {
+                    "sheet_name": str(sheet_name or f"Sheet{idx + 1}"),
+                    "table_name": table_name,
+                    "rows": int(len(writable.index)),
+                    "cols": int(len(cols)),
+                    "columns": list(cols),
+                    "column_map": col_map,
+                }
+            )
+    payload = {
+        "table_id": tid,
+        "attachment_id": str(attachment_id or ""),
+        "name": str(name or ""),
+        "rows": int(default_rows),
+        "cols": int(default_cols),
+        "columns": default_columns,
+        "default_table": default_table,
+        "sheets": sheet_rows,
+        "db_path": str(db),
+    }
+    meta.write_text(json.dumps(payload, ensure_ascii=False), encoding="utf-8")
+    return payload
+
+
+def get_table_meta(table_id: str) -> dict[str, Any] | None:
+    p = _meta_path(str(table_id or "").strip())
+    if not p.exists():
+        return None
+    try:
+        obj = json.loads(p.read_text(encoding="utf-8"))
+        return obj if isinstance(obj, dict) else None
+    except Exception:
+        return None
+
+
+def _resolve_sheet_table(meta: dict[str, Any], sheet: str | None) -> str:
+    default_table = str(meta.get("default_table") or "rows_data")
+    if not sheet:
+        return default_table
+    target = str(sheet).strip()
+    if not target:
+        return default_table
+    sheets = meta.get("sheets") if isinstance(meta.get("sheets"), list) else []
+    for row in sheets:
+        if not isinstance(row, dict):
+            continue
+        if str(row.get("sheet_name") or "") == target or str(row.get("table_name") or "") == target:
+            return str(row.get("table_name") or default_table)
+    return ""
+
+
+def _columns_for_sheet(meta: dict[str, Any], table_name: str) -> list[str]:
+    sheets = meta.get("sheets") if isinstance(meta.get("sheets"), list) else []
+    for row in sheets:
+        if not isinstance(row, dict):
+            continue
+        if str(row.get("table_name") or "") == str(table_name):
+            cols = row.get("columns")
+            if isinstance(cols, list):
+                return [str(x) for x in cols]
+    cols = meta.get("columns")
+    if isinstance(cols, list):
+        return [str(x) for x in cols]
+    return []
+
+
+def query_table(
+    *,
+    table_id: str,
+    columns: list[str] | None = None,
+    limit: int = 50,
+    offset: int = 0,
+    where_contains: dict[str, str] | None = None,
+    sheet: str | None = None,
+) -> dict[str, Any]:
+    meta = get_table_meta(table_id)
+    if not isinstance(meta, dict):
+        return {"ok": False, "error": "table_not_found"}
+    db = Path(str(meta.get("db_path") or ""))
+    if not db.exists():
+        return {"ok": False, "error": "table_store_missing"}
+    table_name = _resolve_sheet_table(meta, sheet)
+    if not table_name:
+        return {"ok": False, "error": "sheet_not_found"}
+    known_cols = _columns_for_sheet(meta, table_name)
+    use_cols = [c for c in (columns or known_cols) if c in known_cols]
+    if not use_cols:
+        use_cols = known_cols[: min(10, len(known_cols))]
+    lim = max(1, min(int(limit or 50), 200))
+    off = max(0, int(offset or 0))
+    select_sql = ", ".join([f'"{c}"' for c in use_cols]) if use_cols else "*"
+    where_sql = ""
+    params: list[Any] = []
+    wc = where_contains if isinstance(where_contains, dict) else {}
+    wc_col = str(wc.get("column") or "").strip()
+    wc_kw = str(wc.get("keyword") or "").strip()
+    if wc_col and wc_kw and wc_col in known_cols:
+        where_sql = f' WHERE "{wc_col}" LIKE ? '
+        params.append(f"%{wc_kw}%")
+    sql = f'SELECT {select_sql} FROM "{table_name}"{where_sql} LIMIT ? OFFSET ?'
+    params.extend([lim, off])
+    out_rows: list[dict[str, Any]] = []
+    total = 0
+    out_rows, engine = _run_select_sql(db_path=db, sql=sql, params=params)
+    with sqlite3.connect(str(db)) as conn:
+        total = int(conn.execute(f'SELECT COUNT(1) AS n FROM "{table_name}"').fetchone()[0] or 0)
+    return {
+        "ok": True,
+        "table_id": str(table_id),
+        "name": str(meta.get("name") or ""),
+        "sheet": str(sheet or ""),
+        "table_name": table_name,
+        "rows_total": total,
+        "columns": use_cols,
+        "rows": out_rows,
+        "limit": lim,
+        "offset": off,
+        "engine": engine,
+    }
+
+
+def aggregate_table(
+    *,
+    table_id: str,
+    metric: str,
+    target_column: str | None = None,
+    group_by: str | None = None,
+    where_contains: dict[str, str] | None = None,
+    top_n: int = 20,
+    sheet: str | None = None,
+) -> dict[str, Any]:
+    meta = get_table_meta(table_id)
+    if not isinstance(meta, dict):
+        return {"ok": False, "error": "table_not_found"}
+    db = Path(str(meta.get("db_path") or ""))
+    if not db.exists():
+        return {"ok": False, "error": "table_store_missing"}
+    table_name = _resolve_sheet_table(meta, sheet)
+    if not table_name:
+        return {"ok": False, "error": "sheet_not_found"}
+    known_cols = _columns_for_sheet(meta, table_name)
+    mt = str(metric or "").strip().lower()
+    if mt not in {"count", "sum", "avg"}:
+        return {"ok": False, "error": "invalid_metric"}
+    grp = str(group_by or "").strip()
+    if grp and grp not in known_cols:
+        return {"ok": False, "error": "invalid_group_by"}
+    tgt = str(target_column or "").strip()
+    if mt in {"sum", "avg"} and tgt not in known_cols:
+        return {"ok": False, "error": "target_column_required"}
+    where_sql = ""
+    params: list[Any] = []
+    wc = where_contains if isinstance(where_contains, dict) else {}
+    wc_col = str(wc.get("column") or "").strip()
+    wc_kw = str(wc.get("keyword") or "").strip()
+    if wc_col and wc_kw and wc_col in known_cols:
+        where_sql = f' WHERE "{wc_col}" LIKE ? '
+        params.append(f"%{wc_kw}%")
+    lim = max(1, min(int(top_n or 20), 200))
+    if mt == "count":
+        metric_sql = "COUNT(1)"
+    else:
+        metric_sql = f'{"SUM" if mt == "sum" else "AVG"}(CAST(NULLIF("{tgt}", \'\') AS REAL))'
+    if grp:
+        sql = (
+            f'SELECT "{grp}" AS group_key, {metric_sql} AS metric_value '
+            f'FROM "{table_name}"{where_sql} GROUP BY "{grp}" '
+            "ORDER BY metric_value DESC LIMIT ?"
+        )
+        params.append(lim)
+        raw_rows, engine = _run_select_sql(db_path=db, sql=sql, params=params)
+        rows: list[dict[str, Any]] = []
+        for r in raw_rows:
+            rows.append(
+                {
+                    "group": r.get("group_key"),
+                    "value": float(r.get("metric_value") or 0.0),
+                }
+            )
+        return {
+            "ok": True,
+            "table_id": str(table_id),
+            "sheet": str(sheet or ""),
+            "table_name": table_name,
+            "metric": mt,
+            "target_column": tgt if mt in {"sum", "avg"} else None,
+            "group_by": grp,
+            "rows": rows,
+            "top_n": lim,
+            "engine": engine,
+        }
+    sql = f'SELECT {metric_sql} AS metric_value FROM "{table_name}"{where_sql} '
+    raw_rows, engine = _run_select_sql(db_path=db, sql=sql, params=params)
+    row = raw_rows[0] if raw_rows else {}
+    return {
+        "ok": True,
+        "table_id": str(table_id),
+        "sheet": str(sheet or ""),
+        "table_name": table_name,
+        "metric": mt,
+        "target_column": tgt if mt in {"sum", "avg"} else None,
+        "group_by": "",
+        "value": float((row.get("metric_value") if isinstance(row, dict) else 0.0) or 0.0),
+        "engine": engine,
+    }
+
+
+def run_table_sql(
+    *,
+    table_id: str,
+    sql: str,
+    limit: int = 200,
+    sheet: str | None = None,
+) -> dict[str, Any]:
+    meta = get_table_meta(table_id)
+    if not isinstance(meta, dict):
+        return {"ok": False, "error": "table_not_found"}
+    db = Path(str(meta.get("db_path") or ""))
+    if not db.exists():
+        return {"ok": False, "error": "table_store_missing"}
+    table_name = _resolve_sheet_table(meta, sheet)
+    if not table_name:
+        return {"ok": False, "error": "sheet_not_found"}
+    raw = str(sql or "").strip()
+    if not raw:
+        return {"ok": False, "error": "sql_required"}
+    lowered = raw.lower()
+    deny = ("insert ", "update ", "delete ", "drop ", "alter ", "attach ", "detach ", "pragma ", "create ")
+    if any(k in lowered for k in deny):
+        return {"ok": False, "error": "sql_not_readonly"}
+    if ";" in raw:
+        return {"ok": False, "error": "sql_multiple_statements_forbidden"}
+    if not (lowered.startswith("select ") or lowered.startswith("with ")):
+        return {"ok": False, "error": "sql_must_start_with_select_or_with"}
+    lim = max(1, min(int(limit or 200), 500))
+    auto_limited = " limit " not in lowered
+    sql_targeted = re.sub(r"(?i)\brows_data\b", f'"{table_name}"', raw)
+    safe_sql = sql_targeted if not auto_limited else f"{sql_targeted}\nLIMIT {lim}"
+    timeout_ms = _tabular_sql_timeout_ms()
+    try:
+        rows, engine = _run_select_sql(db_path=db, sql=safe_sql, params=[], timeout_ms=timeout_ms)
+    except TimeoutError:
+        return {
+            "ok": False,
+            "error": "sql_timeout",
+            "table_id": str(table_id),
+            "name": str(meta.get("name") or ""),
+            "sheet": str(sheet or ""),
+            "table_name": table_name,
+            "input_sql": raw,
+            "executed_sql": safe_sql,
+            "sql_guard": {
+                "readonly_enforced": True,
+                "multi_statement_forbidden": True,
+                "auto_limit_applied": bool(auto_limited),
+                "result_row_cap": lim,
+                "timeout_ms": int(timeout_ms),
+                "timeout_hit": True,
+            },
+        }
+    out_rows = rows[:lim]
+    return {
+        "ok": True,
+        "table_id": str(table_id),
+        "name": str(meta.get("name") or ""),
+        "sheet": str(sheet or ""),
+        "table_name": table_name,
+        "input_sql": raw,
+        "executed_sql": safe_sql,
+        "sql_guard": {
+            "readonly_enforced": True,
+            "multi_statement_forbidden": True,
+            "auto_limit_applied": bool(auto_limited),
+            "result_row_cap": lim,
+            "timeout_ms": int(timeout_ms),
+            "timeout_hit": False,
+        },
+        "rows": out_rows,
+        "rows_returned": int(len(out_rows)),
+        "limit": lim,
+        "engine": engine,
+    }
+
+
+def analyze_table_full_scan(
+    *,
+    table_id: str,
+    columns: list[str] | None = None,
+    sheet: str | None = None,
+    top_values_limit: int = 3,
+) -> dict[str, Any]:
+    meta = get_table_meta(table_id)
+    if not isinstance(meta, dict):
+        return {"ok": False, "error": "table_not_found"}
+    db = Path(str(meta.get("db_path") or ""))
+    if not db.exists():
+        return {"ok": False, "error": "table_store_missing"}
+    table_name = _resolve_sheet_table(meta, sheet)
+    if not table_name:
+        return {"ok": False, "error": "sheet_not_found"}
+    known_cols = _columns_for_sheet(meta, table_name)
+    if not known_cols:
+        return {"ok": False, "error": "table_columns_missing"}
+    use_cols = [c for c in (columns or known_cols) if c in known_cols]
+    if not use_cols:
+        return {"ok": False, "error": "columns_not_found"}
+    top_lim = max(0, min(int(top_values_limit or 0), 10))
+    started = time.perf_counter()
+    with sqlite3.connect(str(db)) as conn:
+        total_rows = int(conn.execute(f'SELECT COUNT(1) FROM "{table_name}"').fetchone()[0] or 0)
+        col_stats: list[dict[str, Any]] = []
+        for col in use_cols:
+            row = conn.execute(
+                (
+                    f'SELECT '
+                    f'SUM(CASE WHEN NULLIF(TRIM("{col}"), \'\') IS NOT NULL THEN 1 ELSE 0 END) AS non_empty_rows, '
+                    f'COUNT(DISTINCT NULLIF(TRIM("{col}"), \'\')) AS distinct_values '
+                    f'FROM "{table_name}"'
+                )
+            ).fetchone()
+            non_empty_rows = int((row[0] if row else 0) or 0)
+            distinct_values = int((row[1] if row else 0) or 0)
+            item: dict[str, Any] = {
+                "column": col,
+                "non_empty_rows": non_empty_rows,
+                "empty_rows": max(0, int(total_rows - non_empty_rows)),
+                "distinct_values": distinct_values,
+                "coverage_ratio": (float(non_empty_rows) / float(total_rows)) if total_rows > 0 else 0.0,
+            }
+            if top_lim > 0:
+                tops = conn.execute(
+                    (
+                        f'SELECT "{col}" AS val, COUNT(1) AS n '
+                        f'FROM "{table_name}" '
+                        f'WHERE NULLIF(TRIM("{col}"), \'\') IS NOT NULL '
+                        f'GROUP BY "{col}" ORDER BY n DESC LIMIT ?'
+                    ),
+                    [top_lim],
+                ).fetchall()
+                item["top_values"] = [{"value": str(r[0]), "count": int(r[1] or 0)} for r in tops]
+            col_stats.append(item)
+    elapsed_ms = int((time.perf_counter() - started) * 1000)
+    return {
+        "ok": True,
+        "table_id": str(table_id),
+        "name": str(meta.get("name") or ""),
+        "sheet": str(sheet or ""),
+        "table_name": table_name,
+        "rows_scanned": int(total_rows),
+        "columns_analyzed": list(use_cols),
+        "column_stats": col_stats,
+        "scan_audit": {
+            "full_scan": True,
+            "rows_scanned": int(total_rows),
+            "columns_count": int(len(use_cols)),
+            "elapsed_ms": int(elapsed_ms),
+        },
+        "engine": "builtin_sqlite_fullscan",
+    }
+
+
+__all__ = [
+    "save_dataframe",
+    "save_workbook",
+    "get_table_meta",
+    "query_table",
+    "aggregate_table",
+    "run_table_sql",
+    "analyze_table_full_scan",
+]
+

platform/integrations/cc_mini_vendor.py

@@ -0,0 +1,85 @@
+"""Locate and optionally launch the ``vendor/cc-mini`` git submodule.
+
+Upstream lives at https://github.com/e10nMa2k/cc-mini — keep it as a submodule
+rather than copying sources into this tree. At the time of integration the
+submodule repository did not ship a root ``LICENSE`` file; treat compliance as
+upstream-owned until one is published.
+"""
+
+from __future__ import annotations
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+from oclaw.platform.config.paths import PROJECT_ROOT
+
+
+def cc_mini_root() -> Path:
+    """Path to the ``cc-mini`` checkout (``<repo>/vendor/cc-mini``)."""
+    return (PROJECT_ROOT / "vendor" / "cc-mini").resolve()
+
+
+def cc_mini_src_dir() -> Path:
+    """Directory that must be on ``PYTHONPATH`` for ``python -m tui.app``."""
+    return (cc_mini_root() / "src").resolve()
+
+
+def cc_mini_available() -> bool:
+    """True when the submodule tree looks present (``src/core/engine.py``)."""
+    return (cc_mini_src_dir() / "core" / "engine.py").is_file()
+
+
+def cc_mini_pythonpath_env(base: dict[str, str] | None = None) -> dict[str, str]:
+    """Return env with ``vendor/cc-mini/src`` prepended to ``PYTHONPATH``."""
+    env = dict(base or os.environ)
+    src = str(cc_mini_src_dir())
+    prev = (env.get("PYTHONPATH") or "").strip()
+    env["PYTHONPATH"] = src + (os.pathsep + prev if prev else "")
+    return env
+
+
+def prepend_cc_mini_src_to_sys_path() -> bool:
+    """Insert ``vendor/cc-mini/src`` at the front of ``sys.path`` if available.
+
+    Prefer subprocess + :func:`cc_mini_pythonpath_env` when isolation matters;
+    importing ``core`` / ``features`` from cc-mini can clash with similarly
+    named packages elsewhere on ``sys.path``.
+    """
+    src = cc_mini_src_dir()
+    if not cc_mini_available():
+        return False
+    s = str(src)
+    if s not in sys.path:
+        sys.path.insert(0, s)
+    return True
+
+
+def run_cc_mini_cli(
+    argv: list[str] | None = None,
+    *,
+    cwd: Path | None = None,
+    env: dict[str, str] | None = None,
+    **kwargs: Any,
+) -> subprocess.CompletedProcess[str]:
+    """Run cc-mini's interactive entry (``python -m tui.app``) like the ``cc-mini`` console script.
+
+    Requires ``cc_mini_available()``; raises ``FileNotFoundError`` if the checkout is missing.
+    """
+    root = cc_mini_root()
+    if not cc_mini_available():
+        raise FileNotFoundError(
+            f"cc-mini not found under {root}; run: git submodule update --init vendor/cc-mini"
+        )
+    merged = cc_mini_pythonpath_env(env if env is not None else os.environ)
+    cmd = [sys.executable, "-m", "tui.app", *(argv or [])]
+    return subprocess.run(
+        cmd,
+        cwd=str(cwd or root),
+        env=merged,
+        check=False,
+        text=True,
+        **kwargs,
+    )

platform/integrations/wecom_client.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+from oclaw.platform.persistence.sqlite_store import SqliteStore
+
+
+class WeComClient:
+    """WeCom AI bot (websocket / bot_api only)."""
+
+    def __init__(self, store: SqliteStore):
+        self.store = store
+
+    def _bot_cfg(self) -> tuple[str, str]:
+        bot_id = str(self.store.get_setting("wecom_bot_id") or "").strip()
+        bot_secret = str(self.store.get_secret("wecom_bot_secret") or self.store.get_setting("wecom_bot_secret") or "").strip()
+        if bot_id and bot_secret:
+            return bot_id, bot_secret
+        if bot_id:
+            row = self.store.find_user_by_channel_account(channel="wecom", account_id=bot_id)
+            if row:
+                tid = str(row["tenant_id"])
+                uid = str(row["user_id"])
+                sk = f"wecom:bot_secret:{tid}:{uid}:{bot_id}"
+                sec = str(self.store.get_secret(sk) or "").strip()
+                if sec:
+                    return bot_id, sec
+        try:
+            tenants = self.store.list_tenants(limit=1)
+            tenant_id = str((tenants[0] or {}).get("id") or "") if tenants else ""
+            if not tenant_id:
+                return bot_id, bot_secret
+            admin = self.store.get_user_by_username(tenant_id=tenant_id, username="administrator")
+            user_id = str((admin or {}).get("id") or "")
+            if not user_id:
+                return bot_id, bot_secret
+            accounts = self.store.list_user_channel_accounts(
+                tenant_id=tenant_id, user_id=user_id, channel="wecom", include_inactive=False
+            )
+            if not accounts:
+                return bot_id, bot_secret
+            aid = str(accounts[0].get("account_id") or "").strip()
+            if not aid:
+                return bot_id, bot_secret
+            scoped_key = f"wecom:bot_secret:{tenant_id}:{user_id}:{aid}"
+            sec = str(self.store.get_secret(scoped_key) or "").strip()
+            if aid and sec:
+                return aid, sec
+        except Exception:
+            return bot_id, bot_secret
+        return bot_id, bot_secret
+
+    def mode_for_account(self, account_id: str) -> str:  # noqa: ARG002
+        return "bot_api"
+
+    def mode(self) -> str:
+        return "bot_api"
+
+    def get_bot_credentials(self) -> tuple[str, str]:
+        bot_id, bot_secret = self._bot_cfg()
+        if not bot_id or not bot_secret:
+            raise RuntimeError("missing wecom bot config: wecom_bot_id / wecom_bot_secret")
+        return bot_id, bot_secret
+
+
+__all__ = ["WeComClient"]

platform/llm/OPENCLAW_MIT_LICENSE.txt

@@ -0,0 +1,27 @@
+MIT License
+
+Copyright (c) 2025 Peter Steinberger (OpenClaw)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+---
+
+The Python modules `tool_call_id.py` and `replay_policy.py` in this directory
+implement replay / tool-call-id handling inspired by OpenClaw's
+`src/agents/tool-call-id.ts` and `src/agents/transcript-policy.ts`.

platform/llm/__init__.py

@@ -0,0 +1 @@
+# oclaw.platform.llm package

platform/llm/chat_models.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+"""Compatibility re-export layer.
+
+The codebase imports `ChatModel/OpenAIChatModel/...` from this module.
+Implementations live under `src.platform.llm.transports.*`.
+"""
+
+import os
+
+from oclaw.platform.llm.tool_schema import shrink_openai_tools_payload_for_api
+from oclaw.platform.llm.tool_schema import openai_tools_json_byte_length as _openai_tools_json_byte_length
+from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall, normalize_image_b64_payload
+from oclaw.platform.llm.transports.google_gemini_sse import GoogleGeminiChatModel
+from oclaw.platform.llm.transports.openai_chat_completions import (
+    OpenAIChatModel,
+    _likely_gemini_openai_compat_base_url,
+    _model_id_suggests_gemini,
+)
+from oclaw.platform.llm.transports.simple import RuleBasedChatModel, StaticTextChatModel
+
+# Backward-compat alias used by `src/chat/agent.py`.
+_normalize_image_b64_payload = normalize_image_b64_payload
+
+
+def gemini_openai_compat_client(model: ChatModel) -> bool:
+    if not isinstance(model, OpenAIChatModel):
+        return False
+    return _likely_gemini_openai_compat_base_url(getattr(model, "base_url", None)) or _model_id_suggests_gemini(
+        getattr(model, "model", None)
+    )
+
+
+def build_default_model() -> ChatModel:
+    mode = (os.getenv("AIA_ASSISTANT_MODE") or "").strip().lower()
+    if mode == "rule":
+        return RuleBasedChatModel()
+    if mode == "ollama":
+        try:
+            from oclaw.agents.factory import DEFAULT_OLLAMA_BASE_URL, DEFAULT_OLLAMA_MODEL
+
+            return OpenAIChatModel(model=DEFAULT_OLLAMA_MODEL, api_key="ollama", base_url=DEFAULT_OLLAMA_BASE_URL)
+        except Exception:
+            return RuleBasedChatModel()
+    if mode == "openai" or mode == "":
+        try:
+            return OpenAIChatModel()
+        except Exception:
+            return RuleBasedChatModel()
+    # Provider selection is normally handled via LLM profiles in `src/agents/factory.py`.
+    return RuleBasedChatModel()
+
+
+__all__ = [
+    "ChatModel",
+    "LLMResponse",
+    "LLMToolCall",
+    "OpenAIChatModel",
+    "GoogleGeminiChatModel",
+    "RuleBasedChatModel",
+    "StaticTextChatModel",
+    "build_default_model",
+    "gemini_openai_compat_client",
+    "normalize_image_b64_payload",
+    "_normalize_image_b64_payload",
+    "shrink_openai_tools_payload_for_api",
+    "_openai_tools_json_byte_length",
+]
+

platform/llm/image_message_client.py

@@ -0,0 +1,685 @@
+from __future__ import annotations
+
+import base64
+import io
+import os
+import time
+from typing import Any
+
+import httpx
+from PIL import Image
+from oclaw.prompts import render_prompt
+
+
+def _summarize_data_url(url: str) -> dict[str, Any]:
+    """
+    Summarize data URLs without logging raw base64 payload.
+    """
+    s = str(url or "")
+    if not s.startswith("data:"):
+        return {"kind": "url", "prefix": s[:30]}
+    mime = "image/jpeg"
+    try:
+        # data:{mime};base64,{b64}
+        head = s.split(";", 1)[0]  # data:image/xxx
+        if head.startswith("data:"):
+            mime = head[len("data:") :] or mime
+    except Exception:
+        pass
+    b64_part = s.split(",", 1)[1] if "," in s else ""
+    has_b64 = ";base64," in s
+    return {"kind": "data_url", "mime": mime, "has_base64": has_b64, "b64_len": len(b64_part)}
+
+
+def _summarize_dashscope_messages(messages: list[dict[str, Any]], *, max_items: int = 3) -> dict[str, Any]:
+    """
+    Debug-only summary of messages[0].content structure.
+    """
+    if not messages or not isinstance(messages, list):
+        return {"content_items": []}
+    first = messages[0] if messages else {}
+    if not isinstance(first, dict):
+        return {"content_items": []}
+    content = first.get("content")
+    if not isinstance(content, list):
+        return {"content_items": []}
+    out: list[dict[str, Any]] = []
+    for it in content[:max_items]:
+        if not isinstance(it, dict):
+            out.append({"item_type": "non_dict"})
+            continue
+        item: dict[str, Any] = {"keys": sorted([str(k) for k in it.keys()])}
+        if "type" in it:
+            item["type"] = it.get("type")
+        if "image" in it:
+            item["image"] = _summarize_data_url(str(it.get("image") or ""))
+        if "image_url" in it:
+            iu = it.get("image_url")
+            if isinstance(iu, dict) and "url" in iu:
+                item["image_url"] = _summarize_data_url(str(iu.get("url") or ""))
+            else:
+                item["image_url"] = {"kind": "image_url_non_dict"}
+        if "text" in it:
+            item["text_len"] = len(str(it.get("text") or ""))
+        out.append(item)
+    return {"content_items": out}
+
+
+def _join_url(base: str, path: str) -> str:
+    b = (base or "").rstrip("/")
+    p = (path or "").lstrip("/")
+    return f"{b}/{p}"
+
+
+def _is_data_url(s: str) -> bool:
+    return s.startswith("data:") and ";base64," in s
+
+
+def _compress_data_url_image(
+    data_url: str,
+    *,
+    max_side: int = 1600,
+    max_bytes: int = 2 * 1024 * 1024,
+    jpeg_quality: int = 82,
+) -> str:
+    """Best-effort compress image data URLs to reduce transport failures."""
+    if not _is_data_url(data_url):
+        return data_url
+    try:
+        header, b64 = data_url.split(";base64,", 1)
+        mime = header.replace("data:", "", 1).strip() or "image/jpeg"
+        raw = base64.b64decode(b64.encode("ascii"))
+        if len(raw) <= max_bytes:
+            return data_url
+
+        with Image.open(io.BytesIO(raw)) as im:
+            im = im.convert("RGB")
+            w, h = im.size
+            longest = max(w, h)
+            if longest > max_side:
+                scale = max_side / float(longest)
+                nw, nh = max(1, int(w * scale)), max(1, int(h * scale))
+                im = im.resize((nw, nh), Image.Resampling.LANCZOS)
+
+            out = io.BytesIO()
+            # Use JPEG for broad compatibility and smaller payload.
+            im.save(out, format="JPEG", quality=max(30, min(95, int(jpeg_quality))), optimize=True)
+            blob = out.getvalue()
+            if not blob:
+                return data_url
+            b64_new = base64.b64encode(blob).decode("ascii")
+            return f"data:{'image/jpeg' if mime.startswith('image/') else mime};base64,{b64_new}"
+    except Exception:
+        return data_url
+
+
+def _post_with_retry(
+    client: httpx.Client,
+    *,
+    url: str,
+    headers: dict[str, str],
+    payload: dict[str, Any],
+    retries: int = 3,
+    backoff_sec: float = 0.8,
+) -> httpx.Response:
+    last_exc: Exception | None = None
+    for attempt in range(1, max(1, retries) + 1):
+        try:
+            return client.post(url, headers=headers, json=payload)
+        except (httpx.ReadError, httpx.ConnectError, httpx.TimeoutException) as e:
+            last_exc = e
+            if attempt >= retries:
+                break
+            time.sleep(backoff_sec * (2 ** (attempt - 1)))
+    assert last_exc is not None
+    raise last_exc
+
+
+def _extract_text_and_images(resp_json: dict[str, Any]) -> tuple[str, list[str]]:
+    text_parts: list[str] = []
+    images: list[str] = []
+    choices = resp_json.get("choices")
+    if isinstance(choices, list) and choices:
+        msg = choices[0].get("message") if isinstance(choices[0], dict) else None
+        if isinstance(msg, dict):
+            c = msg.get("content")
+            if isinstance(c, str):
+                text_parts.append(c)
+            elif isinstance(c, list):
+                for it in c:
+                    if not isinstance(it, dict):
+                        continue
+                    if isinstance(it.get("text"), str):
+                        text_parts.append(str(it.get("text")))
+                    elif isinstance(it.get("image"), str):
+                        images.append(str(it.get("image")))
+                    elif isinstance(it.get("image_url"), str):
+                        images.append(str(it.get("image_url")))
+                    elif isinstance(it.get("image_url"), dict) and isinstance(it["image_url"].get("url"), str):
+                        images.append(str(it["image_url"]["url"]))
+                    elif isinstance(it.get("b64_json"), str):
+                        images.append(f"data:image/png;base64,{it['b64_json']}")
+
+    # fallback for image APIs that return data[0].b64_json/url
+    if not images:
+        data = resp_json.get("data")
+        if isinstance(data, list):
+            for it in data:
+                if not isinstance(it, dict):
+                    continue
+                if isinstance(it.get("url"), str):
+                    images.append(str(it.get("url")))
+                if isinstance(it.get("b64_json"), str):
+                    images.append(f"data:image/png;base64,{it['b64_json']}")
+    return "\n".join([x for x in text_parts if x]).strip(), images
+
+
+def _extract_from_dashscope_response(resp: Any) -> tuple[str, list[str], str]:
+    """Parse dashscope SDK response object (or dict-like) into text/images."""
+    text_parts: list[str] = []
+    images: list[str] = []
+
+    def _as_dict(x: Any) -> dict[str, Any]:
+        if isinstance(x, dict):
+            return x
+        if hasattr(x, "__dict__"):
+            try:
+                return dict(vars(x))
+            except Exception:
+                return {}
+        return {}
+
+    if isinstance(resp, dict):
+        obj = resp
+    else:
+        obj = _as_dict(resp)
+        if not obj and hasattr(resp, "to_dict"):
+            try:
+                obj = resp.to_dict()
+            except Exception:
+                obj = {}
+
+    status_code = int(obj.get("status_code") or getattr(resp, "status_code", 0) or 0)
+    output = obj.get("output") if isinstance(obj.get("output"), dict) else _as_dict(getattr(resp, "output", {}))
+    choices = output.get("choices") if isinstance(output, dict) else None
+    if isinstance(choices, list) and choices:
+        msg = choices[0].get("message") if isinstance(choices[0], dict) else _as_dict(getattr(choices[0], "message", {}))
+        content = msg.get("content")
+        if isinstance(content, list):
+            for it in content:
+                # DashScope SDK sometimes returns content items as objects, not plain dicts.
+                # Convert best-effort so we don't silently drop images.
+                it_d: dict[str, Any]
+                if isinstance(it, dict):
+                    it_d = it
+                else:
+                    it_d = _as_dict(it)
+                if not it_d:
+                    continue
+                if isinstance(it_d.get("text"), str):
+                    text_parts.append(str(it_d["text"]))
+                # image can be str/dict/list depending on SDK variant
+                img_val = it_d.get("image")
+                if isinstance(img_val, str):
+                    images.append(img_val)
+                elif isinstance(img_val, dict):
+                    if isinstance(img_val.get("url"), str):
+                        images.append(str(img_val["url"]))
+                elif isinstance(img_val, list):
+                    for elem in img_val:
+                        if isinstance(elem, str):
+                            images.append(elem)
+                        elif isinstance(elem, dict) and isinstance(elem.get("url"), str):
+                            images.append(str(elem["url"]))
+
+                if isinstance(it_d.get("image_url"), str):
+                    images.append(str(it_d["image_url"]))
+                elif isinstance(it_d.get("image_url"), dict) and isinstance(it_d["image_url"].get("url"), str):
+                    images.append(str(it_d["image_url"]["url"]))
+                elif isinstance(it_d.get("image_url"), list):
+                    for elem in it_d["image_url"]:
+                        if isinstance(elem, str):
+                            images.append(elem)
+                        elif isinstance(elem, dict) and isinstance(elem.get("url"), str):
+                            images.append(str(elem["url"]))
+
+                b64_val = it_d.get("b64_json")
+                if isinstance(b64_val, str):
+                    images.append(f"data:image/png;base64,{b64_val}")
+                elif isinstance(b64_val, list):
+                    for elem in b64_val:
+                        if isinstance(elem, str):
+                            images.append(f"data:image/png;base64,{elem}")
+
+    # fallback for variants returning output.data[{url|b64_json}]
+    if not images and isinstance(output, dict):
+        data = output.get("data")
+        if isinstance(data, list):
+            for it in data:
+                it_d: dict[str, Any]
+                if isinstance(it, dict):
+                    it_d = it
+                else:
+                    it_d = _as_dict(it)
+                if not it_d:
+                    continue
+                url_val = it_d.get("url")
+                if isinstance(url_val, str):
+                    images.append(url_val)
+                elif isinstance(url_val, list):
+                    for elem in url_val:
+                        if isinstance(elem, str):
+                            images.append(elem)
+
+                b64_val = it_d.get("b64_json")
+                if isinstance(b64_val, str):
+                    images.append(f"data:image/png;base64,{b64_val}")
+                elif isinstance(b64_val, list):
+                    for elem in b64_val:
+                        if isinstance(elem, str):
+                            images.append(f"data:image/png;base64,{elem}")
+
+    return "\n".join([x for x in text_parts if x]).strip(), images, str(status_code)
+
+
+def _dashscope_messages(images: list[str], prompt: str) -> list[dict[str, Any]]:
+    prompt_text = str(prompt or "").strip() or render_prompt("image/default_edit_prompt.zh.md", strict=True)
+    content: list[dict[str, Any]] = [{"image": img} for img in images]
+    content.append({"text": prompt_text})
+    return [{"role": "user", "content": content}]
+
+
+def _http_content_blocks(images: list[str], prompt: str, *, typed: bool) -> list[dict[str, Any]]:
+    prompt_text = str(prompt or "").strip() or render_prompt("image/default_edit_prompt.zh.md", strict=True)
+    if not typed:
+        blocks: list[dict[str, Any]] = [{"image": img} for img in images]
+        blocks.append({"text": prompt_text})
+        return blocks
+    # DashScope compatible-mode error is very explicit about `content[*].type` missing.
+    # To align with DashScope's simple schema keys ({"image": ...}, {"text": ...}),
+    # we keep those keys and only add the `type` discriminator.
+    blocks_typed: list[dict[str, Any]] = [{"type": "image", "image": img} for img in images]
+    blocks_typed.append({"type": "text", "text": prompt_text})
+    return blocks_typed
+
+
+def _responses_input_messages(images: list[str], prompt: str) -> list[dict[str, Any]]:
+    prompt_text = str(prompt or "").strip() or render_prompt("image/default_edit_prompt.zh.md", strict=True)
+    content: list[dict[str, Any]] = []
+    for img in images:
+        s = str(img or "").strip()
+        if not s:
+            continue
+        if s.startswith("data:") or s.startswith("http://") or s.startswith("https://"):
+            content.append({"type": "input_image", "image_url": s})
+    if prompt_text:
+        content.append({"type": "input_text", "text": prompt_text})
+    return [{"role": "user", "content": content}]
+
+
+def _dashscope_typed_messages(images: list[str], prompt: str) -> list[dict[str, Any]]:
+    """
+    DashScope compatible-mode often requires a typed schema, where each content item has `type`.
+    Example:
+      {"type":"image_url","image_url":{"url":"..."}}, {"type":"text","text":"..."}
+    """
+    typed_blocks = _http_content_blocks(images, prompt, typed=True)
+    return [{"role": "user", "content": typed_blocks}]
+
+
+def _send_via_dashscope(
+    *,
+    images: list[str],
+    prompt: str,
+    model_name: str,
+    api_key: str | None = None,
+    base_http_api_url: str | None = None,
+    force_typed_schema: bool = False,
+) -> dict[str, Any]:
+    try:
+        import dashscope  # type: ignore
+        from dashscope import MultiModalConversation  # type: ignore
+    except Exception as e:
+        return {"ok": False, "error": f"dashscope package unavailable: {type(e).__name__}: {e}"}
+
+    resolved_key = (api_key or os.getenv("DASHSCOPE_API_KEY") or "").strip()
+    if not resolved_key:
+        return {"ok": False, "error": "missing DASHSCOPE_API_KEY"}
+
+    ds_base = (base_http_api_url or os.getenv("DASHSCOPE_BASE_HTTP_API_URL") or "").strip()
+    if ds_base:
+        dashscope.base_http_api_url = ds_base
+
+    call_kwargs_base: dict[str, Any] = {
+        "api_key": resolved_key,
+        "model": model_name,
+        "stream": False,
+    }
+    # Optional params compatible with user's sample
+    if os.getenv("DASHSCOPE_IMAGE_N"):
+        try:
+            call_kwargs_base["n"] = int(os.getenv("DASHSCOPE_IMAGE_N") or "1")
+        except Exception:
+            pass
+    if os.getenv("DASHSCOPE_IMAGE_WATERMARK"):
+        call_kwargs_base["watermark"] = os.getenv("DASHSCOPE_IMAGE_WATERMARK", "false").lower() in ("1", "true", "yes")
+    if os.getenv("DASHSCOPE_IMAGE_NEGATIVE_PROMPT"):
+        call_kwargs_base["negative_prompt"] = os.getenv("DASHSCOPE_IMAGE_NEGATIVE_PROMPT")
+    if os.getenv("DASHSCOPE_IMAGE_PROMPT_EXTEND"):
+        call_kwargs_base["prompt_extend"] = os.getenv("DASHSCOPE_IMAGE_PROMPT_EXTEND", "true").lower() in ("1", "true", "yes")
+    if os.getenv("DASHSCOPE_IMAGE_SIZE"):
+        call_kwargs_base["size"] = os.getenv("DASHSCOPE_IMAGE_SIZE")
+
+    def _call_with_retry(messages: list[dict[str, Any]]) -> tuple[Any | None, Exception | None]:
+        retries = max(1, int(os.getenv("AIA_IMAGE_RETRIES", "3") or "3"))
+        backoff_sec = float(os.getenv("AIA_IMAGE_RETRY_BACKOFF_SEC", "0.8") or "0.8")
+        last_exc: Exception | None = None
+        resp_obj: Any | None = None
+        for attempt in range(1, retries + 1):
+            try:
+                kwargs = dict(call_kwargs_base)
+                kwargs["messages"] = messages
+                resp_obj = MultiModalConversation.call(**kwargs)
+                return resp_obj, None
+            except Exception as e:
+                last_exc = e
+                if attempt >= retries:
+                    break
+                time.sleep(backoff_sec * (2 ** (attempt - 1)))
+        return None, last_exc
+
+    # DashScope SDK path:
+    # Compatible-mode may require typed content schema (`content[*].type`),
+    # so if we detect compatible-mode, prefer typed schema directly.
+    typed_msgs = _dashscope_typed_messages(images, prompt)
+    simple_msgs = _dashscope_messages(images, prompt)
+
+    first_msgs = typed_msgs if force_typed_schema else simple_msgs
+    second_msgs = simple_msgs if force_typed_schema else typed_msgs
+
+    first_schema = "typed" if first_msgs is typed_msgs else "simple"
+    second_schema = "simple" if first_schema == "typed" else "typed"
+    debug_first = _summarize_dashscope_messages(first_msgs)
+    debug_second = _summarize_dashscope_messages(second_msgs)
+
+    used_schema = first_schema
+    used_debug = debug_first
+
+    resp, err = _call_with_retry(first_msgs)
+    if resp is None:
+        # If the first schema fails without a response, try the other schema once.
+        resp2, err2 = _call_with_retry(second_msgs)
+        if resp2 is None:
+            assert err2 is not None
+            return {
+                "ok": False,
+                "error": (
+                    f"dashscope call failed: {type(err2).__name__}: {err2} "
+                    f"(debug={{first_schema={first_schema},second_schema={second_schema}}})"
+                )[:900],
+            }
+        resp = resp2
+        used_schema = second_schema
+        used_debug = debug_second
+
+    text, out_images, status = _extract_from_dashscope_response(resp)
+    try:
+        status_code = int(status)
+    except Exception:
+        status_code = 0
+    if status_code and status_code != 200:
+        # DashScope may return HTTP 429 (rate limit) as a non-exception status.
+        # _call_with_retry only retries on exceptions, so handle 429 explicitly here.
+        if status_code == 429:
+            status_retries = max(1, int(os.getenv("AIA_IMAGE_STATUS_RETRIES", "4") or "4"))
+            status_backoff_sec = float(os.getenv("AIA_IMAGE_STATUS_RETRY_BACKOFF_SEC", "5.0") or "5.0")
+            for attempt in range(1, status_retries):
+                time.sleep(status_backoff_sec * (2 ** (attempt - 1)))
+                resp_retry, _err_retry = _call_with_retry(simple_msgs)
+                if resp_retry is None:
+                    continue
+                text_r, out_images_r, status_r = _extract_from_dashscope_response(resp_retry)
+                try:
+                    status_r_code = int(status_r)
+                except Exception:
+                    status_r_code = 0
+                if status_r_code == 200 or not status_r_code:
+                    return {
+                        "ok": True,
+                        "text": text_r,
+                        "images": out_images_r,
+                        "backend_shape": "dashscope",
+                    }
+                if status_r_code == 400:
+                    # If we hit schema issues after rate limit, fall back to typed once.
+                    typed_msgs = _dashscope_typed_messages(images, prompt)
+                    resp_t, _err_t = _call_with_retry(typed_msgs)
+                    if resp_t is not None:
+                        text_t, out_images_t, status_t = _extract_from_dashscope_response(resp_t)
+                        try:
+                            status_t_code = int(status_t)
+                        except Exception:
+                            status_t_code = 0
+                        if status_t_code == 200 or not status_t_code:
+                            return {
+                                "ok": True,
+                                "text": text_t,
+                                "images": out_images_t,
+                                "backend_shape": "dashscope",
+                            }
+                    # continue loop for more 429 tries
+            code = getattr(resp, "code", "") or ""
+            msg = getattr(resp, "message", "") or ""
+            return {
+                "ok": False,
+                "error": (
+                    f"dashscope status={status_code} code={code} debug={{used_schema={used_schema},used_debug={used_debug}}} "
+                    f"message={msg[:400]} (after 429 retries)"
+                ),
+                "backend_shape": "dashscope",
+            }
+
+        # Compatible-mode may reject "simple" schema with HTTP 400 and a message like:
+        #   messages[0].content[1].type missing required parameter.
+        # To make this robust, retry typed schema whenever we see 400.
+        if status_code == 400:
+            resp2, _err2 = _call_with_retry(typed_msgs)
+            if resp2 is not None:
+                text2, out_images2, status2 = _extract_from_dashscope_response(resp2)
+                try:
+                    status2_code = int(status2)
+                except Exception:
+                    status2_code = 0
+                if status2_code == 200 or not status2_code:
+                    return {
+                        "ok": True,
+                        "text": text2,
+                        "images": out_images2,
+                        "backend_shape": "dashscope",
+                    }
+
+        code = getattr(resp, "code", "") or ""
+        msg = getattr(resp, "message", "") or ""
+        return {
+            "ok": False,
+            "error": (
+                f"dashscope status={status_code} code={code} debug={{used_schema={used_schema},used_debug={used_debug}}} "
+                f"message={msg[:400]}"
+            ),
+            "backend_shape": "dashscope",
+        }
+    return {
+        "ok": True,
+        "text": text,
+        "images": out_images,
+        "backend_shape": "dashscope",
+        "debug_used_schema": used_schema,
+        "debug_used_debug": used_debug,
+    }
+
+
+def send_image_messages(
+    *,
+    images: list[str],
+    prompt: str,
+    model: str | None = None,
+    timeout_sec: int = 60,
+    api_key: str | None = None,
+    base_url: str | None = None,
+    dashscope_api_key: str | None = None,
+    dashscope_base_http_api_url: str | None = None,
+) -> dict[str, Any]:
+    """Send multimodal messages payload with image/text content blocks.
+
+    Preferred payload: {"messages":[{"role":"user","content":[{"image":"..."},{"text":"..."}]}]}
+    Auto-compat: try multi-image first, then fallback to single `image` field style.
+    """
+    model_name = (
+        model
+        or os.getenv("AIA_IMAGE_MODEL")
+        or os.getenv("DASHSCOPE_IMAGE_MODEL")
+        or os.getenv("OPENAI_MODEL")
+        or "qwen-image-2.0-pro"
+    ).strip()
+    resolved_base_url = (base_url or os.getenv("AIA_IMAGE_BASE_URL") or os.getenv("OPENAI_BASE_URL") or "https://api.openai.com/v1").strip()
+    resolved_api_key = (api_key or os.getenv("AIA_IMAGE_API_KEY") or os.getenv("OPENAI_API_KEY") or "").strip()
+    resolved_ds_key = (dashscope_api_key or os.getenv("DASHSCOPE_API_KEY") or "").strip()
+    resolved_ds_base = (dashscope_base_http_api_url or os.getenv("DASHSCOPE_BASE_HTTP_API_URL") or "").strip()
+    endpoint = (os.getenv("AIA_IMAGE_CHAT_ENDPOINT") or "/chat/completions").strip()
+    url = _join_url(resolved_base_url, endpoint)
+    if not images:
+        return {"ok": False, "error": "at least one image input is required"}
+
+    raw_selected = [str(x).strip() for x in images if str(x).strip()][:3]
+    selected: list[str] = []
+    input_kind: list[str] = []
+    for img in raw_selected:
+        if _is_data_url(img):
+            selected.append(_compress_data_url_image(img))
+            input_kind.append("data_url")
+            continue
+        if img.startswith("http://") or img.startswith("https://"):
+            # Keep URL as-is when caller provides URL input.
+            selected.append(img)
+            input_kind.append("url")
+            continue
+    if not selected:
+        return {"ok": False, "error": "no usable image input (expected URL or data URL)"}
+
+    if resolved_ds_key:
+        # SpecialistAgent already normalizes DashScope SDK calls to native `/api/v1`.
+        # For the SDK path, prefer the official simple schema for both single and
+        # multi-image requests: {"image": ...}, {"text": ...}.
+        #
+        # This avoids a split where multi-image succeeds but single-image still
+        # trips over `messages[0].content[*].type` handling.
+        ds = _send_via_dashscope(
+            images=selected,
+            prompt=prompt,
+            model_name=model_name,
+            api_key=resolved_ds_key,
+            base_http_api_url=resolved_ds_base,
+            force_typed_schema=False,
+        )
+        ds["input_kind"] = input_kind if input_kind else ["data_url"]
+        # When DashScope credentials are present, always return the DashScope path result.
+        # Do not silently fall through to the OpenAI-compatible HTTP path, because that can
+        # mask the real SDK error with a secondary `content[*].type` error from another backend path.
+        return ds
+
+    if not resolved_api_key:
+        return {"ok": False, "error": "missing api key (AIA_IMAGE_API_KEY/OPENAI_API_KEY or DASHSCOPE_API_KEY)"}
+    prefer_typed_http = "compatible-mode" in resolved_base_url.lower()
+    content_multi = _http_content_blocks(selected, prompt, typed=prefer_typed_http)
+    content_multi_fallback = _http_content_blocks(selected, prompt, typed=not prefer_typed_http)
+
+    headers = {
+        "Authorization": f"Bearer {resolved_api_key}",
+        "Content-Type": "application/json",
+    }
+    payload_multi = {
+        "model": model_name,
+        "messages": [{"role": "user", "content": content_multi}],
+    }
+    payload_single = {
+        "model": model_name,
+        "messages": [{"role": "user", "content": content_multi_fallback[-2:] if len(content_multi_fallback) >= 2 else content_multi_fallback}],
+    }
+    responses_payload = {
+        "model": model_name,
+        "input": {"messages": _responses_input_messages(selected, prompt)},
+    }
+
+    with httpx.Client(timeout=float(timeout_sec)) as client:
+        # try multi-image payload first
+        try:
+            r = _post_with_retry(client, url=url, headers=headers, payload=payload_multi)
+        except Exception as e:
+            return {"ok": False, "error": f"http request failed: {type(e).__name__}: {e}", "backend_shape": "multi"}
+        if r.status_code >= 400:
+            # fallback single-image shape for strict backends
+            try:
+                r2 = _post_with_retry(client, url=url, headers=headers, payload=payload_single)
+            except Exception as e:
+                return {
+                    "ok": False,
+                    "error": f"http fallback request failed: {type(e).__name__}: {e}",
+                    "backend_shape": "single-fallback-failed",
+                }
+            if r2.status_code >= 400:
+                body2 = str(r2.text or "")
+                if ("input.messages" in body2) or ("Input should be 'user'" in body2):
+                    # OpenAI-compatible Responses schema fallback.
+                    try:
+                        r3 = _post_with_retry(
+                            client,
+                            url=_join_url(resolved_base_url, "/responses"),
+                            headers=headers,
+                            payload=responses_payload,
+                        )
+                        if r3.status_code < 400:
+                            obj3 = r3.json()
+                            text3, out_images3 = _extract_text_and_images(obj3 if isinstance(obj3, dict) else {})
+                            return {
+                                "ok": True,
+                                "text": text3,
+                                "images": out_images3,
+                                "backend_shape": "responses-fallback",
+                                "input_kind": input_kind if input_kind else ["data_url"],
+                            }
+                    except Exception:
+                        pass
+                return {
+                    "ok": False,
+                    "error": f"http {r2.status_code}: {r2.text[:500]}",
+                    "backend_shape": "single-fallback-failed",
+                }
+            try:
+                obj2 = r2.json()
+            except Exception:
+                return {"ok": False, "error": f"non-json response: {r2.text[:500]}", "backend_shape": "single"}
+            text, out_images = _extract_text_and_images(obj2 if isinstance(obj2, dict) else {})
+            return {
+                "ok": True,
+                "text": text,
+                "images": out_images,
+                "backend_shape": "single",
+                "input_kind": input_kind[-1:] if input_kind else ["data_url"],
+            }
+
+        try:
+            obj = r.json()
+        except Exception:
+            return {"ok": False, "error": f"non-json response: {r.text[:500]}", "backend_shape": "multi"}
+        text, out_images = _extract_text_and_images(obj if isinstance(obj, dict) else {})
+        return {
+            "ok": True,
+            "text": text,
+            "images": out_images,
+            "backend_shape": "multi",
+            "input_kind": input_kind if input_kind else ["data_url"],
+        }
+
+
+__all__ = ["send_image_messages"]
+

platform/llm/replay_policy.py

@@ -0,0 +1,91 @@
+"""Transcript replay policy for OpenAI-compatible Chat Completions.
+
+Inspired by OpenClaw (MIT) `src/agents/transcript-policy.ts` defaults for
+`openai-completions`: enable strict tool-call-id sanitization so proxies and
+multi-provider histories do not break on id format / length.
+
+See OPENCLAW_MIT_LICENSE.txt in this package.
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import Any
+
+from oclaw.platform.llm.tool_call_id import DEFAULT_MAX_OPENAI_TOOL_CALL_ID_LEN
+
+
+@dataclass(frozen=True)
+class ReplayPolicy:
+    """Controls pre-request normalization of chat messages for tool-use flow."""
+
+    enabled: bool
+    sanitize_tool_call_ids: bool
+    repair_tool_pairing: bool
+    max_tool_call_id_len: int
+
+
+def _env_bool(name: str, default: bool) -> bool:
+    raw = str(os.getenv(name) or "").strip().lower()
+    if raw in ("0", "false", "no", "off"):
+        return False
+    if raw in ("1", "true", "yes", "on"):
+        return True
+    return default
+
+
+def resolve_replay_policy(
+    base_url: str | None,
+    model_id: str | None,
+    *,
+    llm_profile_mode: str | None = None,
+) -> ReplayPolicy:
+    """Resolve replay policy for OpenAI-compatible chat completions.
+
+    When enabled (default): sanitize assistant/tool ids before every request.
+    This matches oclaw's strict OpenAI-compatible replay defaults.
+    """
+    del base_url, model_id, llm_profile_mode  # reserved for provider-specific overrides
+
+    if not _env_bool("AIA_REPLAY_POLICY_ENABLED", True):
+        return ReplayPolicy(
+            enabled=False,
+            sanitize_tool_call_ids=False,
+            repair_tool_pairing=False,
+            max_tool_call_id_len=DEFAULT_MAX_OPENAI_TOOL_CALL_ID_LEN,
+        )
+
+    max_len = DEFAULT_MAX_OPENAI_TOOL_CALL_ID_LEN
+    raw_ml = str(os.getenv("AIA_TOOL_CALL_ID_MAX_LEN") or "").strip()
+    if raw_ml.isdigit():
+        max_len = max(8, min(int(raw_ml), 128))
+
+    repair = _env_bool("AIA_REPLAY_REPAIR_TOOL_PAIRING", True)
+
+    return ReplayPolicy(
+        enabled=True,
+        sanitize_tool_call_ids=True,
+        repair_tool_pairing=repair,
+        max_tool_call_id_len=max_len,
+    )
+
+
+def apply_replay_policy_to_messages(
+    messages: list[dict[str, Any]],
+    policy: ReplayPolicy,
+) -> list[dict[str, Any]]:
+    """Apply repair + id sanitization in order."""
+    if not policy.enabled or not messages:
+        return messages
+    from oclaw.platform.llm.tool_call_id import (
+        repair_orphan_tool_messages,
+        rewrite_openai_chat_messages_tool_ids,
+    )
+
+    out = messages
+    if policy.repair_tool_pairing:
+        out = repair_orphan_tool_messages(out)
+    if policy.sanitize_tool_call_ids:
+        out = rewrite_openai_chat_messages_tool_ids(out, max_len=policy.max_tool_call_id_len)
+    return out

platform/llm/tool_call_id.py

@@ -0,0 +1,160 @@
+"""OpenAI-compatible tool_call_id / tool result id rewriting.
+
+Inspired by OpenClaw (MIT) `src/agents/tool-call-id.ts`: sanitize ids to strict
+alphanumeric form and keep assistant.tool_calls[].id paired with role=tool
+tool_call_id in encounter order.
+
+See OPENCLAW_MIT_LICENSE.txt in this package.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import re
+from collections import deque
+from typing import Any, Literal
+
+ToolCallIdMode = Literal["strict", "strict9"]
+
+# OpenAI Chat Completions: tool_calls[].id commonly capped ~40 chars on some gateways.
+DEFAULT_MAX_OPENAI_TOOL_CALL_ID_LEN = 40
+
+
+def _short_hash(text: str, length: int = 8) -> str:
+    return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest()[:length]
+
+
+def sanitize_tool_call_id(raw: str | None, *, mode: ToolCallIdMode = "strict") -> str:
+    """Strip to [a-zA-Z0-9] only; empty input gets a deterministic placeholder."""
+    if not raw or not isinstance(raw, str):
+        return "sanitizedtoolid" if mode == "strict" else "defaultid"
+    s = raw.strip()
+    if not s:
+        return "sanitizedtoolid" if mode == "strict" else "defaultid"
+    alnum = re.sub(r"[^a-zA-Z0-9]", "", s)
+    if mode == "strict9":
+        if len(alnum) >= 9:
+            return alnum[:9]
+        if alnum:
+            return _short_hash(alnum, 9)
+        return _short_hash("sanitized", 9)
+    return alnum if alnum else "sanitizedtoolid"
+
+
+def _make_unique(seed: str, used: set[str], *, max_len: int) -> str:
+    base = sanitize_tool_call_id(seed, mode="strict")[:max_len]
+    if base and base not in used:
+        used.add(base)
+        return base
+    h = _short_hash(seed, 8)
+    for i in range(1000):
+        cand = f"{base[: max(0, max_len - len(h) - 1)]}{h}"[:max_len]
+        if i > 0:
+            suf = f"x{i}"
+            cand = f"{base[: max(0, max_len - len(suf) - len(h))]}{suf}{h}"[:max_len]
+        if cand not in used:
+            used.add(cand)
+            return cand
+    cand = _short_hash(f"{seed}:{id(used)}", max_len)[:max_len]
+    used.add(cand)
+    return cand
+
+
+def rewrite_openai_chat_messages_tool_ids(
+    messages: list[dict[str, Any]],
+    *,
+    max_len: int = DEFAULT_MAX_OPENAI_TOOL_CALL_ID_LEN,
+) -> list[dict[str, Any]]:
+    """Rewrite assistant tool_calls ids and matching tool tool_call_id/call_id.
+
+    Uses a per-raw-id queue (oclaw-style) so repeated raw ids map to distinct
+    new ids in order, and tool results consume the queue FIFO.
+    """
+    if not messages:
+        return messages
+
+    pending: dict[str, deque[str]] = {}
+    used: set[str] = set()
+
+    def _alloc_for_raw(raw: str) -> str:
+        raw = str(raw or "").strip()
+        seed = raw if raw else "empty"
+        return _make_unique(seed, used, max_len=max_len)
+
+    out: list[dict[str, Any]] = []
+    for m in messages:
+        if not isinstance(m, dict):
+            continue
+        role = str(m.get("role") or "")
+        if role == "assistant" and m.get("tool_calls"):
+            mm = dict(m)
+            tcs = mm.get("tool_calls")
+            if isinstance(tcs, list):
+                new_tcs: list[dict[str, Any]] = []
+                for tc in tcs:
+                    if not isinstance(tc, dict):
+                        new_tcs.append(tc)
+                        continue
+                    raw_id = str(tc.get("id") or "").strip()
+                    new_id = _alloc_for_raw(raw_id)
+                    q = pending.setdefault(raw_id, deque())
+                    q.append(new_id)
+                    new_tcs.append({**tc, "id": new_id})
+                mm["tool_calls"] = new_tcs
+            out.append(mm)
+            continue
+        if role == "tool":
+            mm = dict(m)
+            raw_ref = (
+                str(mm.get("tool_call_id") or mm.get("call_id") or "").strip()
+            )
+            new_ref: str | None = None
+            if raw_ref and raw_ref in pending and pending[raw_ref]:
+                new_ref = pending[raw_ref].popleft()
+            elif raw_ref:
+                # Orphan or ordering mismatch: allocate fresh id (best-effort pairing)
+                new_ref = _alloc_for_raw(raw_ref)
+            if new_ref:
+                mm["tool_call_id"] = new_ref
+                # Keep call_id in sync when both present (some gateways expect call_id)
+                if "call_id" in mm:
+                    mm["call_id"] = new_ref
+            else:
+                mm.pop("tool_call_id", None)
+                mm.pop("call_id", None)
+            out.append(mm)
+            continue
+        out.append(dict(m) if isinstance(m, dict) else m)
+    return out
+
+
+def repair_orphan_tool_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Drop tool_call_id/call_id from tool rows that reference no prior assistant tool_calls id."""
+    allowed: set[str] = set()
+    for m in messages:
+        if not isinstance(m, dict):
+            continue
+        if str(m.get("role") or "") != "assistant":
+            continue
+        tcs = m.get("tool_calls")
+        if not isinstance(tcs, list):
+            continue
+        for tc in tcs:
+            if isinstance(tc, dict) and tc.get("id"):
+                allowed.add(str(tc.get("id")))
+
+    out: list[dict[str, Any]] = []
+    for m in messages:
+        if not isinstance(m, dict):
+            out.append(m)
+            continue
+        if str(m.get("role") or "") != "tool":
+            out.append(m)
+            continue
+        mm = dict(m)
+        tid = str(mm.get("tool_call_id") or mm.get("call_id") or "").strip()
+        if tid and tid not in allowed:
+            mm.pop("tool_call_id", None)
+            mm.pop("call_id", None)
+        out.append(mm)
+    return out

platform/llm/tool_schema.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import json
+import logging
+import os
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_MIN_TOOL_PARAMETERS: dict[str, Any] = {"type": "object", "additionalProperties": True}
+
+
+def openai_tools_json_byte_length(tools: list[dict[str, Any]]) -> int:
+    try:
+        return len(json.dumps(tools, ensure_ascii=False, default=str))
+    except Exception:
+        return 0
+
+
+def shrink_openai_tools_payload_for_api(tools: list[dict[str, Any]], *, max_json_bytes: int) -> list[dict[str, Any]]:
+    """Shrink OpenAI ``tools`` JSON for strict OpenAI-compatible gateways."""
+    if not tools or max_json_bytes <= 0:
+        return tools
+    if openai_tools_json_byte_length(tools) <= max_json_bytes:
+        return tools
+    flat: list[dict[str, Any]] = []
+    for t in tools:
+        if not isinstance(t, dict) or str(t.get("type") or "") != "function":
+            continue
+        fn = t.get("function")
+        if not isinstance(fn, dict):
+            continue
+        name = str(fn.get("name") or "").strip()
+        if not name:
+            continue
+        desc = str(fn.get("description") or "")
+        flat.append({"type": "function", "function": {"name": name, "description": desc, "parameters": dict(_MIN_TOOL_PARAMETERS)}})
+    if not flat:
+        return tools
+
+    def _serialized_with_desc_cap(cap: int) -> str:
+        trimmed: list[dict[str, Any]] = []
+        for x in flat:
+            fn0 = x["function"]
+            trimmed.append(
+                {
+                    "type": "function",
+                    "function": {
+                        "name": fn0["name"],
+                        "description": (fn0.get("description") or "")[: max(0, cap)],
+                        "parameters": fn0["parameters"],
+                    },
+                }
+            )
+        return json.dumps(trimmed, ensure_ascii=False, default=str)
+
+    lo, hi = 0, min(8000, max_json_bytes)
+    best_cap = 0
+    while lo <= hi:
+        mid = (lo + hi) // 2
+        s = _serialized_with_desc_cap(mid)
+        if len(s) <= max_json_bytes:
+            best_cap = mid
+            lo = mid + 1
+        else:
+            hi = mid - 1
+    out_obj: list[dict[str, Any]] = json.loads(_serialized_with_desc_cap(best_cap))
+    blob = json.dumps(out_obj, ensure_ascii=False, default=str)
+    dropped = 0
+    while len(out_obj) > 8 and len(blob) > max_json_bytes:
+        out_obj.pop()
+        dropped += 1
+        blob = json.dumps(out_obj, ensure_ascii=False, default=str)
+    logger.warning(
+        "Shrunk OpenAI tools[] for API size limits: count=%d max_json_bytes=%d desc_cap=%d dropped=%d final_bytes=%d",
+        len(out_obj),
+        max_json_bytes,
+        best_cap,
+        dropped,
+        len(blob),
+    )
+    return out_obj
+
+
+def default_max_openai_tools_json_bytes(base_url: str | None) -> int | None:
+    raw = str(os.getenv("AIA_OPENAI_TOOLS_MAX_JSON_CHARS") or "").strip()
+    if raw.isdigit():
+        return max(2000, int(raw))
+    u = (base_url or "").lower()
+    if "dashscope.aliyuncs.com" in u:
+        return 28000
+    if str(os.getenv("AIA_SHRINK_OPENAI_TOOLS") or "").strip().lower() in ("1", "true", "yes", "on"):
+        mx = str(os.getenv("AIA_SHRINK_OPENAI_TOOLS_MAX_JSON") or "28000").strip()
+        return max(2000, int(mx)) if mx.isdigit() else 28000
+    return None
+
+
+__all__ = [
+    "default_max_openai_tools_json_bytes",
+    "openai_tools_json_byte_length",
+    "shrink_openai_tools_payload_for_api",
+]
+

platform/llm/tool_wire_policy.py

@@ -0,0 +1,779 @@
+"""Graduated OpenAI ``tools[]`` shaping + usage-aware omission / per-tool admin policies.
+
+Uses ``tool_log`` aggregates, ``app_setting`` for admin config / per-tool levels / penalty state.
+Wire payload differs from local :class:`~src.tools.base.ToolSpec`; validation still uses full schemas.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+SETTINGS_KEY_PENALTY_STATE = "mcp_tool_wire_penalty_state"
+SETTINGS_KEY_TOOL_POLICIES = "mcp_tool_wire_tool_policies"
+SETTINGS_KEY_ADMIN_CONFIG = "mcp_tool_wire_admin_config"
+
+# Role-scoped (per expert) overrides. Shape:
+# - policies: { "<role>": { "mcp__server__tool": <int level> } }
+# - penalty:  { "<role>": { "mcp__server__tool": <penalty state dict> } }
+SETTINGS_KEY_PENALTY_STATE_BY_ROLE = "mcp_tool_wire_penalty_state_by_role"
+SETTINGS_KEY_TOOL_POLICIES_BY_ROLE = "mcp_tool_wire_tool_policies_by_role"
+SETTINGS_KEY_ROLE_MODE_BY_ROLE = "mcp_tool_wire_role_mode_by_role"
+
+_MIN_PARAMETERS: dict[str, Any] = {"type": "object", "additionalProperties": True}
+
+
+def _env_prefixed(name_suffix: str, default: str = "") -> str:
+    raw = str(os.getenv(f"AIA_{name_suffix}") or "").strip()
+    if raw:
+        return raw
+    raw = str(os.getenv(f"OPS_{name_suffix}") or "").strip()
+    if raw:
+        return raw
+    return str(default).strip()
+
+
+def wire_policy_enabled(base_url: str | None) -> bool:
+    raw = _env_prefixed("MCP_WIRE_USAGE_POLICY", "").lower()
+    if raw in ("1", "true", "yes", "on"):
+        return True
+    if raw in ("0", "false", "no", "off"):
+        return False
+    u = (base_url or "").lower()
+    return "dashscope.aliyuncs.com" in u
+
+
+def penalty_disabled() -> bool:
+    return _env_prefixed("MCP_WIRE_PENALTY_DISABLE", "").lower() in ("1", "true", "yes", "on")
+
+
+def _admin_defaults_from_env() -> dict[str, Any]:
+    return {
+        "wire_policy": "inherit",
+        "top_n_full": int(_env_prefixed("MCP_WIRE_TOP_N_FULL", "20") or "20"),
+        "stale_hours": float(_env_prefixed("MCP_WIRE_STALE_HOURS", "3") or "3"),
+        "penalty_minutes": float(_env_prefixed("MCP_WIRE_PENALTY_MINUTES", "30") or "30"),
+        "medium_rank_start": int(_env_prefixed("MCP_WIRE_MEDIUM_RANK_START", "21") or "21"),
+        "medium_rank_end": int(_env_prefixed("MCP_WIRE_MEDIUM_RANK_END", "50") or "50"),
+        "medium_desc_chars": int(_env_prefixed("MCP_WIRE_MEDIUM_DESC_CHARS", "520") or "520"),
+        "minimal_desc_cap": int(_env_prefixed("MCP_WIRE_MINIMAL_DESC_CAP", "80") or "80"),
+        "penalty_disable": False,
+    }
+
+
+def load_merged_admin_config(store: Any) -> dict[str, Any]:
+    out = _admin_defaults_from_env()
+    try:
+        raw = store.get_setting(SETTINGS_KEY_ADMIN_CONFIG)
+        if not raw:
+            return out
+        d = json.loads(raw) if isinstance(raw, str) else raw
+        if isinstance(d, dict):
+            for k, v in d.items():
+                if k in out or k == "wire_policy":
+                    out[k] = v
+    except Exception:
+        pass
+    pd = out.get("penalty_disable")
+    if isinstance(pd, str):
+        out["penalty_disable"] = pd.strip().lower() in ("1", "true", "yes", "on")
+    return out
+
+
+def load_tool_policies_dict(store: Any) -> dict[str, int]:
+    try:
+        raw = store.get_setting(SETTINGS_KEY_TOOL_POLICIES) or "{}"
+        d = json.loads(raw) if isinstance(raw, str) else raw
+    except Exception:
+        return {}
+    if not isinstance(d, dict):
+        return {}
+    out: dict[str, int] = {}
+    for k, v in d.items():
+        nm = str(k or "").strip()
+        if not nm.startswith("mcp__"):
+            continue
+        try:
+            n = int(v)
+        except (TypeError, ValueError):
+            continue
+        if n == 9999:
+            out[nm] = 9999
+        elif n <= 0:
+            out[nm] = 0
+        else:
+            out[nm] = min(n, 9998)
+    return out
+
+
+def load_tool_policies_dict_for_role(store: Any, *, role: str | None) -> dict[str, int]:
+    """Role-scoped policies; falls back to global if absent/unparseable."""
+    r = str(role or "").strip().lower()
+    if not r:
+        return load_tool_policies_dict(store)
+    try:
+        raw = store.get_setting(SETTINGS_KEY_TOOL_POLICIES_BY_ROLE) or "{}"
+        outer = json.loads(raw) if isinstance(raw, str) else raw
+    except Exception:
+        outer = {}
+    if not isinstance(outer, dict):
+        return load_tool_policies_dict(store)
+    inner = outer.get(r)
+    if not isinstance(inner, dict):
+        return load_tool_policies_dict(store)
+    # Reuse same coercion rules as global loader.
+    out: dict[str, int] = {}
+    for k, v in inner.items():
+        nm = str(k or "").strip()
+        if not nm.startswith("mcp__"):
+            continue
+        try:
+            n = int(v)
+        except (TypeError, ValueError):
+            continue
+        if n == 9999:
+            out[nm] = 9999
+        elif n <= 0:
+            out[nm] = 0
+        else:
+            out[nm] = min(n, 9998)
+    return out
+
+
+def load_penalty_state_for_role(store: Any, *, role: str | None) -> dict[str, Any]:
+    """Role-scoped penalty state; falls back to global if absent/unparseable."""
+    r = str(role or "").strip().lower()
+    if not r:
+        raw_pen = store.get_setting(SETTINGS_KEY_PENALTY_STATE) or "{}"
+        try:
+            pen = json.loads(raw_pen) if isinstance(raw_pen, str) else {}
+        except Exception:
+            pen = {}
+        if not isinstance(pen, dict):
+            pen = {}
+        return migrate_legacy_penalty_store(pen)
+    try:
+        raw = store.get_setting(SETTINGS_KEY_PENALTY_STATE_BY_ROLE) or "{}"
+        outer = json.loads(raw) if isinstance(raw, str) else raw
+    except Exception:
+        outer = {}
+    if not isinstance(outer, dict):
+        return load_penalty_state_for_role(store, role=None)
+    inner = outer.get(r)
+    if not isinstance(inner, dict):
+        return load_penalty_state_for_role(store, role=None)
+    return migrate_legacy_penalty_store(inner)
+
+
+def _persist_penalty_state_for_role(store: Any, *, role: str | None, penalty_state: dict[str, Any]) -> None:
+    r = str(role or "").strip().lower()
+    clean: dict[str, Any] = {}
+    for k, v in (penalty_state or {}).items():
+        if v is None:
+            continue
+        if isinstance(v, dict):
+            clean[str(k)] = v
+    if not r:
+        store.set_setting(SETTINGS_KEY_PENALTY_STATE, json.dumps(clean, ensure_ascii=False))
+        return
+    try:
+        raw = store.get_setting(SETTINGS_KEY_PENALTY_STATE_BY_ROLE) or "{}"
+        outer = json.loads(raw) if isinstance(raw, str) else raw
+    except Exception:
+        outer = {}
+    if not isinstance(outer, dict):
+        outer = {}
+    outer[r] = clean
+    store.set_setting(SETTINGS_KEY_PENALTY_STATE_BY_ROLE, json.dumps(outer, ensure_ascii=False))
+
+
+def wire_graduation_effective(base_url: str | None, admin: dict[str, Any]) -> bool:
+    mode = str(admin.get("wire_policy") or "inherit").strip().lower()
+    if mode in ("always", "on", "true", "1", "yes"):
+        return True
+    if mode in ("never", "off", "false", "0", "no"):
+        return False
+    return wire_policy_enabled(base_url)
+
+
+def penalty_effective_disabled(admin: dict[str, Any]) -> bool:
+    if bool(admin.get("penalty_disable")):
+        return True
+    return penalty_disabled()
+
+
+def migrate_legacy_penalty_store(raw: dict[str, Any]) -> dict[str, Any]:
+    """Older rows used ``omit_until`` without ``phase``; add ``kind`` for global tier."""
+    out: dict[str, Any] = {}
+    for k, v in (raw or {}).items():
+        if not isinstance(v, dict):
+            continue
+        v2 = dict(v)
+        if str(v2.get("phase") or "") in ("active", "done"):
+            if "kind" not in v2:
+                v2["kind"] = "d"
+            out[str(k)] = v2
+            continue
+        ou_s = str(v2.get("omit_until") or "")
+        ws = str(v2.get("wave_ts") or "")
+        ou = _parse_iso(ou_s)
+        if ou_s and ou:
+            if _utc_now() < ou:
+                v2 = {"phase": "active", "omit_until": ou_s, "wave_ts": ws, "kind": v2.get("kind") or "d"}
+            else:
+                v2 = {"phase": "done", "wave_ts": ws, "kind": v2.get("kind") or "d"}
+        elif ws:
+            v2 = {"phase": "done", "wave_ts": ws, "kind": v2.get("kind") or "d"}
+        else:
+            continue
+        out[str(k)] = v2
+    return out
+
+
+def _parse_iso(ts: str) -> datetime | None:
+    s = str(ts or "").strip()
+    if not s:
+        return None
+    try:
+        if s.endswith("Z"):
+            s = s[:-1] + "+00:00"
+        return datetime.fromisoformat(s)
+    except Exception:
+        return None
+
+
+def _utc_now() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+def _wire_json_size(tools: list[dict[str, Any]]) -> int:
+    try:
+        return len(json.dumps(tools, ensure_ascii=False, default=str))
+    except Exception:
+        return 0
+
+
+def _strip_nested_descriptions(obj: Any) -> Any:
+    if isinstance(obj, dict):
+        out: dict[str, Any] = {}
+        for k, v in obj.items():
+            if str(k) == "description":
+                continue
+            out[str(k)] = _strip_nested_descriptions(v)
+        return out
+    if isinstance(obj, list):
+        return [_strip_nested_descriptions(x) for x in obj]
+    return obj
+
+
+def _tier_medium(fn: dict[str, Any], *, cfg: dict[str, Any]) -> dict[str, Any]:
+    mid_desc_len = int(cfg.get("medium_desc_chars") or 520)
+    mid_desc_len = max(80, min(mid_desc_len, 4000))
+    desc = str(fn.get("description") or "")[:mid_desc_len]
+    params = fn.get("parameters")
+    if isinstance(params, dict):
+        params_out = dict(_strip_nested_descriptions(params))
+    else:
+        params_out = dict(_MIN_PARAMETERS)
+    return {"name": fn.get("name"), "description": desc, "parameters": params_out}
+
+
+def _tier_minimal(fn: dict[str, Any], *, desc_cap: int) -> dict[str, Any]:
+    desc = str(fn.get("description") or "")[: max(0, desc_cap)]
+    return {"name": fn.get("name"), "description": desc, "parameters": dict(_MIN_PARAMETERS)}
+
+
+def _clone_tool_entry(t: dict[str, Any]) -> dict[str, Any]:
+    try:
+        return json.loads(json.dumps(t, ensure_ascii=False, default=str))
+    except Exception:
+        return dict(t)
+
+
+def _is_builtin_tool_name(name: str) -> bool:
+    return not str(name).startswith("mcp__")
+
+
+def _normalize_role_mode(v: Any) -> str:
+    s = str(v or "").strip().lower()
+    if s in {"unrestricted", "forbidden", "restricted"}:
+        return s
+    return "restricted"
+
+
+def load_role_mode_for_role(store: Any, *, role: str | None) -> str:
+    r = str(role or "").strip().lower()
+    if not r:
+        return "restricted"
+    try:
+        raw = store.get_setting(SETTINGS_KEY_ROLE_MODE_BY_ROLE) or "{}"
+        obj = json.loads(raw) if isinstance(raw, str) else raw
+    except Exception:
+        obj = {}
+    if not isinstance(obj, dict):
+        return "restricted"
+    return _normalize_role_mode(obj.get(r))
+
+
+def _is_stale_hours(last_ts_iso: str | None, stale_hours: float) -> bool:
+    if not last_ts_iso:
+        return False
+    dt = _parse_iso(last_ts_iso)
+    if dt is None:
+        return False
+    return (_utc_now() - dt).total_seconds() > stale_hours * 3600.0
+
+
+def _idle_minutes_exceeded(last_ts_iso: str | None, idle_minutes: float) -> bool:
+    if not last_ts_iso:
+        return False
+    dt = _parse_iso(last_ts_iso)
+    if dt is None:
+        return False
+    return (_utc_now() - dt).total_seconds() > idle_minutes * 60.0
+
+
+def _wave_penalty_omit(
+    name: str,
+    last_ts: str | None,
+    idle_hit: bool,
+    penalty_minutes: float,
+    st_nm: Any,
+    out_penalty_updates: dict[str, Any],
+    *,
+    kind: str,
+) -> bool:
+    pw = last_ts or ""
+    mins = max(1.0, min(float(penalty_minutes), 24 * 60))
+    if not idle_hit:
+        return False
+    prev = out_penalty_updates.get(name)
+    if isinstance(prev, dict) and str(prev.get("kind") or "") != str(kind):
+        out_penalty_updates.pop(name, None)
+        st_nm = None
+    else:
+        st_nm = prev
+
+    omit = False
+    if isinstance(st_nm, dict) and str(st_nm.get("phase") or "") == "active":
+        ou = _parse_iso(str(st_nm.get("omit_until") or ""))
+        if ou and _utc_now() < ou:
+            omit = True
+        else:
+            out_penalty_updates[name] = {"phase": "done", "wave_ts": pw, "kind": str(kind)}
+    elif isinstance(st_nm, dict) and str(st_nm.get("phase") or "") == "done" and str(st_nm.get("wave_ts") or "") == pw:
+        pass
+    else:
+        until = (_utc_now() + timedelta(minutes=mins)).isoformat()
+        out_penalty_updates[name] = {"phase": "active", "omit_until": until, "wave_ts": pw, "kind": str(kind)}
+        omit = True
+    return omit
+
+
+def filter_permanent_ban_mcp_tools(tools: list[dict[str, Any]], policies: dict[str, int]) -> list[dict[str, Any]]:
+    out: list[dict[str, Any]] = []
+    for entry in tools:
+        if not isinstance(entry, dict) or str(entry.get("type") or "") != "function":
+            out.append(entry)
+            continue
+        fn = entry.get("function")
+        if not isinstance(fn, dict):
+            out.append(entry)
+            continue
+        name = str(fn.get("name") or "").strip()
+        if not name or _is_builtin_tool_name(name):
+            out.append(entry)
+            continue
+        if policies.get(name) == 9999:
+            continue
+        out.append(entry)
+    return out
+
+
+def prepare_openai_tools_for_llm_api(
+    tools: list[dict[str, Any]],
+    *,
+    base_url: str | None,
+    max_json_bytes: int | None,
+    store: Any | None = None,
+    role: str | None = None,
+) -> list[dict[str, Any]]:
+    """Build wire ``tools[]``: optional graduated policy + byte cap."""
+
+    def _fallback_shrink(raw: list[dict[str, Any]], cap: int) -> list[dict[str, Any]]:
+        from oclaw.platform.llm.tool_schema import shrink_openai_tools_payload_for_api
+
+        return shrink_openai_tools_payload_for_api(raw, max_json_bytes=cap)
+
+    if not tools:
+        return tools
+
+    policies: dict[str, int] = {}
+    admin: dict[str, Any] = _admin_defaults_from_env()
+    role_mode = "restricted"
+    try:
+        if store is None:
+            from oclaw.platform.config.paths import db_path
+            from oclaw.platform.persistence.sqlite_store import SqliteStore
+
+            store = SqliteStore(db_path())
+        # Role-scoped policies if configured; otherwise fall back to global.
+        policies = load_tool_policies_dict_for_role(store, role=str(role or "").strip().lower() or None)
+        admin = load_merged_admin_config(store)
+        role_mode = load_role_mode_for_role(store, role=str(role or "").strip().lower() or None)
+    except Exception as exc:
+        logger.warning("tool_wire_policy: load admin/policies (%s)", exc)
+
+    def _filter_mcp_out(raw: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        out: list[dict[str, Any]] = []
+        for entry in raw:
+            if not isinstance(entry, dict) or str(entry.get("type") or "") != "function":
+                out.append(entry)
+                continue
+            fn = entry.get("function")
+            if not isinstance(fn, dict):
+                out.append(entry)
+                continue
+            nm = str(fn.get("name") or "").strip()
+            if nm.startswith("mcp__"):
+                continue
+            out.append(entry)
+        return out
+
+    tools_pass1 = filter_permanent_ban_mcp_tools(tools, policies)
+
+    if role_mode == "forbidden":
+        out = _filter_mcp_out(tools)
+        if max_json_bytes is not None and max_json_bytes > 0:
+            return _fallback_shrink(out, max_json_bytes)
+        return out
+    if role_mode == "unrestricted":
+        if max_json_bytes is not None and max_json_bytes > 0:
+            return _fallback_shrink(tools_pass1, max_json_bytes)
+        return tools_pass1
+
+    if not wire_graduation_effective(base_url, admin):
+        if max_json_bytes is not None and max_json_bytes > 0:
+            return _fallback_shrink(tools_pass1, max_json_bytes)
+        return tools_pass1
+
+    top_n = int(admin.get("top_n_full") or 20)
+    top_n = max(3, min(top_n, 80))
+    stale_h = float(admin.get("stale_hours") or 3)
+    stale_h = max(0.25, min(stale_h, 720.0))
+    penalty_min_global = float(admin.get("penalty_minutes") or 30)
+    penalty_min_global = max(1.0, min(penalty_min_global, 24 * 60))
+    medium_floor = int(admin.get("medium_rank_start") or 21)
+    medium_ceiling = int(admin.get("medium_rank_end") or 50)
+    if medium_floor > medium_ceiling:
+        medium_floor, medium_ceiling = medium_ceiling, medium_floor
+
+    usage_map: dict[str, dict[str, Any]] = {}
+    penalty_state: dict[str, Any] = {}
+    try:
+        usage_map = store.list_mcp_tool_aggregate_usage()
+        penalty_state = load_penalty_state_for_role(store, role=str(role or "").strip().lower() or None)
+    except Exception as exc:
+        logger.warning("tool_wire_policy: usage store unavailable (%s); fallback shrink only", exc)
+        if max_json_bytes is not None and max_json_bytes > 0:
+            return _fallback_shrink(tools_pass1, max_json_bytes)
+        return tools_pass1
+
+    ranked = sorted(
+        usage_map.items(),
+        key=lambda kv: (-int(kv[1].get("count") or 0), str(kv[1].get("last_ts") or "")),
+    )
+    rank_by_name = {nm: idx + 1 for idx, (nm, _) in enumerate(ranked)}
+    top_full = {nm for nm, _ in ranked[:top_n]}
+
+    out_penalty_updates: dict[str, Any] = dict(penalty_state)
+    omit_set: set[str] = set()
+
+    for nm in list(out_penalty_updates.keys()):
+        meta = usage_map.get(nm)
+        cur_ts = str(meta.get("last_ts") or "") if meta else ""
+        st = out_penalty_updates.get(nm)
+        if not isinstance(st, dict):
+            continue
+        w_ts = str(st.get("wave_ts") or "")
+        if w_ts and cur_ts and w_ts != cur_ts:
+            out_penalty_updates.pop(nm, None)
+
+    pen_glob = penalty_effective_disabled(admin)
+    built: list[dict[str, Any]] = []
+    for entry in tools_pass1:
+        if not isinstance(entry, dict) or str(entry.get("type") or "") != "function":
+            built.append(entry)
+            continue
+        fn = entry.get("function")
+        if not isinstance(fn, dict):
+            built.append(entry)
+            continue
+        name = str(fn.get("name") or "").strip()
+        if not name:
+            built.append(entry)
+            continue
+
+        if _is_builtin_tool_name(name):
+            built.append(_clone_tool_entry(entry))
+            continue
+
+        usage = usage_map.get(name) or {}
+        last_ts = str(usage.get("last_ts") or "") or None
+        rk = rank_by_name.get(name)
+
+        policy_lv = policies.get(name)
+        st_nm = out_penalty_updates.get(name)
+
+        # policy_lv semantics (punishment only):
+        # - None: inherit global graduation + idle penalty.
+        # - 0: exempt this tool from idle penalty (but still participates in graduation tiers).
+        # - 1..9998: per-tool idle/penalty tuning (N * 10 minutes) + still participates in graduation tiers.
+        # - 9999: permanent ban (filtered earlier in filter_permanent_ban_mcp_tools).
+        omit = False
+        if policy_lv == 0:
+            omit = False
+        elif policy_lv is not None and policy_lv >= 1:
+            idle_m = float(policy_lv) * 10.0
+            pen_m = float(policy_lv) * 10.0
+            idle_hit = _idle_minutes_exceeded(last_ts, idle_m)
+            kind = f"L{int(policy_lv)}"
+            if not pen_glob:
+                omit = _wave_penalty_omit(
+                    name, last_ts, idle_hit, pen_m, st_nm, out_penalty_updates, kind=kind
+                )
+        else:
+            idle_hit = _is_stale_hours(last_ts, stale_h)
+            exempt = name in top_full
+            if not pen_glob and idle_hit and not exempt:
+                omit = _wave_penalty_omit(
+                    name,
+                    last_ts,
+                    idle_hit,
+                    penalty_min_global,
+                    st_nm,
+                    out_penalty_updates,
+                    kind="d",
+                )
+
+        if omit:
+            omit_set.add(name)
+            continue
+
+        tier = "minimal"
+        if name in top_full:
+            tier = "full"
+        elif rk is not None and medium_floor <= rk <= medium_ceiling:
+            tier = "medium"
+        else:
+            tier = "minimal"
+
+        if tier == "full":
+            built.append(_clone_tool_entry(entry))
+        elif tier == "medium":
+            mf = _tier_medium(fn, cfg=admin)
+            built.append({"type": "function", "function": mf})
+        else:
+            cap0 = int(admin.get("minimal_desc_cap") or 80)
+            cap0 = max(0, min(cap0, 2000))
+            mf = _tier_minimal(fn, desc_cap=cap0)
+            built.append({"type": "function", "function": mf})
+
+    try:
+        _persist_penalty_state_for_role(
+            store,
+            role=str(role or "").strip().lower() or None,
+            penalty_state=out_penalty_updates,
+        )
+    except Exception as exc:
+        logger.warning("tool_wire_policy: persist penalty state failed: %s", exc)
+
+    if omit_set:
+        logger.info(
+            "tool_wire_policy: omitted %d MCP tools during penalty window: %s",
+            len(omit_set),
+            sorted(omit_set)[:12],
+        )
+
+    if max_json_bytes is not None and max_json_bytes > 0 and _wire_json_size(built) > max_json_bytes:
+        return _squeeze_to_budget(built, max_json_bytes, admin=admin)
+
+    return built
+
+
+def _squeeze_to_budget(
+    built: list[dict[str, Any]], max_json_bytes: int, *, admin: dict[str, Any]
+) -> list[dict[str, Any]]:
+    work = json.loads(json.dumps(built, ensure_ascii=False, default=str))
+
+    def sz() -> int:
+        return _wire_json_size(work)
+
+    if sz() <= max_json_bytes:
+        return work
+    for i, ent in enumerate(work):
+        fn = ent.get("function") if isinstance(ent.get("function"), dict) else {}
+        ps = fn.get("parameters")
+        if isinstance(ps, dict) and len(json.dumps(ps, ensure_ascii=False)) > 400:
+            nm = str(fn.get("name") or "")
+            if nm.startswith("mcp__"):
+                cap = int(admin.get("minimal_desc_cap") or 80)
+                work[i] = {
+                    "type": "function",
+                    "function": _tier_minimal(fn, desc_cap=max(0, cap)),
+                }
+        if sz() <= max_json_bytes:
+            return work
+
+    for cap in (60, 40, 24, 12, 8, 4, 0):
+        for i, ent in enumerate(work):
+            fn = ent.get("function") if isinstance(ent.get("function"), dict) else {}
+            nm = str(fn.get("name") or "")
+            if nm.startswith("mcp__"):
+                work[i] = {"type": "function", "function": _tier_minimal(fn, desc_cap=cap)}
+        if sz() <= max_json_bytes:
+            return work
+
+    idxs = [i for i, e in enumerate(work) if str((e.get("function") or {}).get("name") or "").startswith("mcp__")]
+    for i in reversed(idxs):
+        if sz() <= max_json_bytes:
+            break
+        work.pop(i)
+
+    if sz() > max_json_bytes:
+        from oclaw.platform.llm.tool_schema import shrink_openai_tools_payload_for_api
+
+        work = shrink_openai_tools_payload_for_api(work, max_json_bytes=max_json_bytes)
+    return work
+
+
+def penalty_row_status(
+    *,
+    wire_name: str,
+    policy_level: int | None,
+    penalty: dict[str, Any] | None,
+    last_ts: str | None,
+) -> dict[str, Any]:
+    """Human-readable row for Admin UI."""
+    if policy_level is None:
+        return {
+            "phase": "inherit",
+            "omit_until": None,
+            "unblock_hint": "未配置：走全局线侧与闲置惩罚（与显式写入 0 不同）",
+            "kind": None,
+        }
+    if policy_level == 9999:
+        return {
+            "phase": "permanent_ban",
+            "omit_until": None,
+            "unblock_hint": "永久不上送（需将策略改为非 9999）",
+            "kind": None,
+        }
+    if policy_level == 0:
+        return {
+            "phase": "exempt",
+            "omit_until": None,
+            "unblock_hint": "策略 0：不参与闲置惩罚（仍可能受线侧分层/压缩影响）",
+            "kind": None,
+        }
+    st = penalty.get(wire_name) if isinstance(penalty, dict) else None
+    if not isinstance(st, dict):
+        return {"phase": "none", "omit_until": None, "unblock_hint": "-", "kind": None}
+    ph = str(st.get("phase") or "")
+    ou = str(st.get("omit_until") or "")
+    kd = str(st.get("kind") or "")
+    if ph == "active" and ou:
+        dt = _parse_iso(ou)
+        hint = f"惩罚中，预计解封（omit_until）: {ou}"
+        if dt and _utc_now() >= dt:
+            hint = "惩罚窗口应已结束；下次请求会写入 done"
+        return {"phase": "active", "omit_until": ou, "unblock_hint": hint, "kind": kd}
+    if ph == "done":
+        return {
+            "phase": "done",
+            "omit_until": None,
+            "unblock_hint": "本轮已服完刑；再次闲置达到阈值后会重新惩罚",
+            "kind": kd,
+        }
+    return {"phase": ph or "unknown", "omit_until": ou or None, "unblock_hint": "-", "kind": kd or None}
+
+
+def build_tool_wire_snapshot(store: Any, *, role: str | None = None) -> dict[str, Any]:
+    """Aggregate MCP install list + usage + policies for Admin GET."""
+    from oclaw.tools.mcp.registry import McpRegistry
+
+    admin = load_merged_admin_config(store)
+    role_norm = str(role or "").strip().lower() or None
+    role_mode = load_role_mode_for_role(store, role=role_norm)
+    policies = load_tool_policies_dict_for_role(store, role=role_norm)
+    pen = load_penalty_state_for_role(store, role=role_norm)
+    usage = store.list_mcp_tool_aggregate_usage()
+    servers = McpRegistry(store).list_servers(enabled_only=False)
+    tools_out: list[dict[str, Any]] = []
+    for s in servers:
+        sid = str(s.get("server_id") or "").strip()
+        if not sid:
+            continue
+        for t in store.list_mcp_server_tools(server_id=sid):
+            short = str(t.get("tool_name") or "").strip()
+            if not short:
+                continue
+            wire = f"mcp__{sid}__{short}"
+            agg = usage.get(wire) or {}
+            in_db = wire in policies
+            lv = int(policies[wire]) if in_db else None
+            st = penalty_row_status(
+                wire_name=wire,
+                policy_level=lv,
+                penalty=pen,
+                last_ts=str(agg.get("last_ts") or "") or None,
+            )
+            tools_out.append(
+                {
+                    "server_id": sid,
+                    "mcp_tool_name": short,
+                    "wire_name": wire,
+                    "policy_level": lv,
+                    "policy_in_db": in_db,
+                    "count": int(agg.get("count") or 0),
+                    "last_ts": str(agg.get("last_ts") or ""),
+                    "penalty": st,
+                    "raw_penalty": pen.get(wire) if isinstance(pen.get(wire), dict) else None,
+                }
+            )
+    tools_out.sort(key=lambda x: (x["server_id"], x["wire_name"]))
+    return {
+        "ok": True,
+        "role": role_norm or "",
+        "role_mode": role_mode,
+        "config": admin,
+        "policies": policies,
+        "penalty_state": pen,
+        "tools": tools_out,
+    }
+
+
+__all__ = [
+    "SETTINGS_KEY_PENALTY_STATE",
+    "SETTINGS_KEY_TOOL_POLICIES",
+    "SETTINGS_KEY_ADMIN_CONFIG",
+    "SETTINGS_KEY_PENALTY_STATE_BY_ROLE",
+    "SETTINGS_KEY_TOOL_POLICIES_BY_ROLE",
+    "SETTINGS_KEY_ROLE_MODE_BY_ROLE",
+    "build_tool_wire_snapshot",
+    "filter_permanent_ban_mcp_tools",
+    "load_merged_admin_config",
+    "load_tool_policies_dict",
+    "load_tool_policies_dict_for_role",
+    "load_penalty_state_for_role",
+    "load_role_mode_for_role",
+    "migrate_legacy_penalty_store",
+    "penalty_row_status",
+    "prepare_openai_tools_for_llm_api",
+    "wire_policy_enabled",
+]

platform/llm/transports/__init__.py

@@ -0,0 +1 @@
+# oclaw.platform.llm.transports package

platform/llm/transports/anthropic_messages.py

@@ -0,0 +1,204 @@
+from __future__ import annotations
+
+import json
+import logging
+import os
+import uuid
+from typing import Any, Optional
+from collections.abc import Callable, Iterable
+
+from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall
+
+logger = logging.getLogger(__name__)
+
+
+def _as_dict(obj: Any) -> dict[str, Any] | None:
+    if obj is None:
+        return None
+    if isinstance(obj, dict):
+        return obj
+    if hasattr(obj, "model_dump"):
+        try:
+            d = obj.model_dump(mode="python")
+            return d if isinstance(d, dict) else None
+        except Exception:
+            return None
+    return None
+
+
+def parse_anthropic_stream_events(
+    events: Iterable[Any],
+    *,
+    on_token: Optional[Callable[[str], None]] = None,
+) -> tuple[str, list[LLMToolCall]]:
+    parts: list[str] = []
+    tool_by_id: dict[str, dict[str, Any]] = {}
+    current_block: dict[str, Any] | None = None
+
+    for ev in events:
+        d = _as_dict(ev) or {}
+        typ = str(d.get("type") or "")
+        if typ in ("message_start", "message_delta", "message_stop"):
+            continue
+        if typ == "content_block_start":
+            cb = d.get("content_block") if isinstance(d.get("content_block"), dict) else {}
+            current_block = cb
+            # tool_use blocks carry name/id/input in start
+            if str(cb.get("type") or "") == "tool_use":
+                tid = str(cb.get("id") or "") or f"call_{uuid.uuid4().hex}"
+                tool_by_id[tid] = {
+                    "id": tid,
+                    "name": str(cb.get("name") or ""),
+                    "input_json": cb.get("input") if isinstance(cb.get("input"), dict) else {},
+                    "_input_buf": "",
+                }
+            continue
+        if typ == "content_block_delta":
+            delta = d.get("delta") if isinstance(d.get("delta"), dict) else {}
+            dt = str(delta.get("type") or "")
+            if dt == "text_delta":
+                t = str(delta.get("text") or "")
+                if t:
+                    parts.append(t)
+                    if on_token:
+                        on_token(t)
+                continue
+            if dt in ("input_json_delta", "tool_use_delta"):
+                partial = delta.get("partial_json") or delta.get("partial") or ""
+                s = str(partial or "")
+                # attach to last tool_use block if any
+                # Anthropics uses content block index; we just buffer by most recent tool_use id.
+                if current_block and str(current_block.get("type") or "") == "tool_use":
+                    tid = str(current_block.get("id") or "")
+                    if tid and tid in tool_by_id:
+                        tool_by_id[tid]["_input_buf"] = str(tool_by_id[tid].get("_input_buf") or "") + s
+                continue
+            continue
+        if typ == "content_block_stop":
+            current_block = None
+            continue
+
+    tool_calls: list[LLMToolCall] = []
+    for tid, slot in tool_by_id.items():
+        args = slot.get("input_json") if isinstance(slot.get("input_json"), dict) else {}
+        buf = str(slot.get("_input_buf") or "").strip()
+        if buf:
+            try:
+                parsed = json.loads(buf)
+                if isinstance(parsed, dict):
+                    args = parsed
+                else:
+                    args = {"_raw": parsed}
+            except Exception:
+                # keep best-effort
+                pass
+        tool_calls.append(LLMToolCall(id=tid, name=str(slot.get("name") or ""), arguments=args, thought_signature=None))
+
+    return "".join(parts), tool_calls
+
+
+class AnthropicMessagesModel(ChatModel):
+    def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
+        self.model = (model or os.getenv("ANTHROPIC_MODEL") or "claude-3-5-sonnet-latest").strip()
+        self.api_key = (api_key or os.getenv("ANTHROPIC_API_KEY") or "").strip()
+        self.base_url = (base_url or os.getenv("ANTHROPIC_BASE_URL") or "").strip() or None
+        if not self.api_key:
+            raise RuntimeError("未设置 ANTHROPIC_API_KEY，无法使用 Anthropic 模型")
+        try:
+            import anthropic
+        except Exception as exc:
+            raise RuntimeError("未安装 anthropic 依赖，请先 pip install -r requirements.txt") from exc
+        kw: dict[str, Any] = {"api_key": self.api_key}
+        if self.base_url:
+            kw["base_url"] = self.base_url
+        self._client = anthropic.Anthropic(**kw)
+
+    @staticmethod
+    def _to_anthropic_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
+        system_chunks: list[str] = []
+        out: list[dict[str, Any]] = []
+        for m in messages or []:
+            if not isinstance(m, dict):
+                continue
+            role = str(m.get("role") or "")
+            content = m.get("content")
+            if role == "system":
+                if isinstance(content, str) and content.strip():
+                    system_chunks.append(content)
+                continue
+            if role in ("user", "assistant"):
+                if isinstance(content, str):
+                    out.append({"role": role, "content": content})
+                else:
+                    out.append({"role": role, "content": str(content)})
+                continue
+            if role == "tool":
+                # Anthropic expects tool results as user content blocks; we stringify.
+                name = str(m.get("name") or m.get("tool_name") or m.get("toolName") or "tool").strip()
+                out.append({"role": "user", "content": f"[tool_result:{name}] {str(content)}"})
+                continue
+        return "\n\n".join(system_chunks).strip(), out
+
+    @staticmethod
+    def _to_anthropic_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        out: list[dict[str, Any]] = []
+        for t in tools or []:
+            if not isinstance(t, dict) or str(t.get("type") or "") != "function":
+                continue
+            fn = t.get("function")
+            if not isinstance(fn, dict):
+                continue
+            name = str(fn.get("name") or "").strip()
+            if not name:
+                continue
+            out.append(
+                {
+                    "name": name,
+                    "description": str(fn.get("description") or ""),
+                    "input_schema": fn.get("parameters") if isinstance(fn.get("parameters"), dict) else {"type": "object"},
+                }
+            )
+        return out
+
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        system, msgs = self._to_anthropic_messages(messages)
+        atools = self._to_anthropic_tools(tools)
+        try:
+            stream = self._client.messages.stream(
+                model=self.model,
+                system=system or None,
+                messages=msgs,
+                tools=atools or None,
+                max_tokens=2048,
+            )
+            text, tool_calls = parse_anthropic_stream_events(stream, on_token=on_token)
+            return LLMResponse(content=text, tool_calls=tool_calls)
+        except Exception as exc:
+            logger.info("anthropic stream failed; fallback to non-stream (%s)", exc)
+            resp = self._client.messages.create(
+                model=self.model,
+                system=system or None,
+                messages=msgs,
+                tools=atools or None,
+                max_tokens=2048,
+            )
+            d = _as_dict(resp) or {}
+            content = d.get("content")
+            text = ""
+            if isinstance(content, list):
+                for blk in content:
+                    if isinstance(blk, dict) and blk.get("type") == "text":
+                        text += str(blk.get("text") or "")
+            if on_token and text:
+                on_token(text)
+            return LLMResponse(content=text, tool_calls=[])
+
+
+__all__ = ["AnthropicMessagesModel", "parse_anthropic_stream_events"]
+

platform/llm/transports/base.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+import base64
+import json
+from dataclasses import dataclass
+from typing import Any, Optional
+from collections.abc import Callable
+
+
+@dataclass(frozen=True)
+class LLMToolCall:
+    id: str
+    name: str
+    arguments: dict[str, Any]
+    # Gemini OpenAI-compat and native transports may require preserving a signature across tool loops.
+    thought_signature: str | None = None
+
+
+@dataclass(frozen=True)
+class LLMResponse:
+    content: str
+    tool_calls: list[LLMToolCall]
+    reasoning_content: str = ""
+
+
+class ChatModel:
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        raise NotImplementedError
+
+
+def coerce_thought_signature_for_storage(v: Any) -> str | None:
+    if v is None:
+        return None
+    if isinstance(v, str):
+        return v
+    if isinstance(v, (bytes, bytearray)):
+        try:
+            return bytes(v).decode("utf-8")
+        except UnicodeDecodeError:
+            return base64.b64encode(bytes(v)).decode("ascii")
+    if isinstance(v, (dict, list)):
+        return json.dumps(v, separators=(",", ":"), ensure_ascii=False)
+    return str(v)
+
+
+def normalize_image_b64_payload(raw: Any) -> str:
+    if raw is None:
+        return ""
+    if isinstance(raw, (bytes, bytearray)):
+        return base64.b64encode(bytes(raw)).decode("ascii")
+    s = str(raw).strip()
+    if not s:
+        return ""
+    if s.startswith("data:") and ";base64," in s:
+        s = s.split(";base64,", 1)[-1].strip()
+    return "".join(s.split())
+

platform/llm/transports/google_gemini_sse.py

@@ -0,0 +1,265 @@
+from __future__ import annotations
+
+import json
+import os
+import uuid
+from typing import Any, Optional
+from collections.abc import Callable
+
+from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall
+
+
+class GoogleGeminiChatModel(ChatModel):
+    """Native Google Gemini streaming transport (SSE), modeled after OpenClaw's google transport."""
+
+    def __init__(self, *, model: str, api_key: str, base_url: str | None = None):
+        self.model = (model or "").strip() or "gemini-2.5-pro"
+        self.api_key = (api_key or "").strip()
+        self.base_url = (base_url or "").strip() or "https://generativelanguage.googleapis.com/v1beta"
+        if not self.api_key:
+            raise RuntimeError("未设置 GOOGLE_API_KEY，无法使用 Gemini 原生接口")
+
+    def _tool_decls_from_openai_tools(self, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        decls: list[dict[str, Any]] = []
+        for t in tools or []:
+            if not isinstance(t, dict) or str(t.get("type") or "") != "function":
+                continue
+            fn = t.get("function")
+            if not isinstance(fn, dict):
+                continue
+            name = str(fn.get("name") or "").strip()
+            if not name:
+                continue
+            decls.append(
+                {
+                    "name": name,
+                    "description": str(fn.get("description") or ""),
+                    # OpenClaw uses `parametersJsonSchema`
+                    "parametersJsonSchema": fn.get("parameters") if isinstance(fn.get("parameters"), dict) else {"type": "object"},
+                }
+            )
+        return decls
+
+    @staticmethod
+    def _thinking_config_from_env() -> dict[str, Any] | None:
+        enabled = str(os.getenv("AIA_GEMINI_THINKING") or "").strip().lower()
+        if enabled in ("0", "false", "no", "off"):
+            return {"includeThoughts": False}
+        if enabled in ("1", "true", "yes", "on"):
+            cfg: dict[str, Any] = {"includeThoughts": True}
+            lvl = str(os.getenv("AIA_GEMINI_THINKING_LEVEL") or "").strip()
+            if lvl:
+                cfg["thinkingLevel"] = lvl
+            bud = str(os.getenv("AIA_GEMINI_THINKING_BUDGET") or "").strip()
+            if bud.isdigit():
+                cfg["thinkingBudget"] = int(bud)
+            return cfg
+        return None
+
+    @staticmethod
+    def _parse_tool_result_payload(raw: Any) -> Any:
+        if raw is None:
+            return {}
+        if isinstance(raw, (dict, list)):
+            return raw
+        s = str(raw or "").strip()
+        if not s:
+            return {}
+        try:
+            return json.loads(s)
+        except Exception:
+            return {"raw": s}
+
+    @staticmethod
+    def _openai_messages_to_gemini_contents(messages: list[dict[str, Any]]) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]:
+        system_parts: list[dict[str, Any]] = []
+        contents: list[dict[str, Any]] = []
+
+        def _append(role: str, parts: list[dict[str, Any]]) -> None:
+            if not parts:
+                return
+            contents.append({"role": role, "parts": parts})
+
+        for m in messages or []:
+            if not isinstance(m, dict):
+                continue
+            role = str(m.get("role") or "")
+            content = m.get("content")
+            if role == "system":
+                if isinstance(content, str) and content.strip():
+                    system_parts.append({"text": content})
+                continue
+            if role == "user":
+                parts: list[dict[str, Any]] = []
+                if isinstance(content, str):
+                    if content.strip():
+                        parts.append({"text": content})
+                elif isinstance(content, list):
+                    for item in content:
+                        if not isinstance(item, dict):
+                            continue
+                        if item.get("type") == "text" and str(item.get("text") or "").strip():
+                            parts.append({"text": str(item.get("text") or "")})
+                _append("user", parts)
+                continue
+            if role == "assistant":
+                parts: list[dict[str, Any]] = []
+                if isinstance(content, str) and content.strip():
+                    parts.append({"text": content})
+                _append("model", parts)
+                continue
+            if role == "tool":
+                name = str(m.get("name") or "").strip() or str(m.get("tool_name") or m.get("toolName") or "").strip()
+                payload = GoogleGeminiChatModel._parse_tool_result_payload(content)
+                _append("user", [{"functionResponse": {"name": name or "tool", "response": payload}}])
+                continue
+
+        system_instruction = {"parts": system_parts} if system_parts else None
+        return system_instruction, contents
+
+    def _build_url(self) -> str:
+        base = self._normalize_base_url_for_native_sse(self.base_url).rstrip("/")
+        model_path = self.model
+        if not model_path.startswith("models/") and not model_path.startswith("tunedModels/"):
+            model_path = f"models/{model_path}"
+        # Prefer header auth (OpenClaw-style); avoid forcing key into query string.
+        return f"{base}/{model_path}:streamGenerateContent?alt=sse"
+
+    @staticmethod
+    def _normalize_base_url_for_native_sse(base_url: str) -> str:
+        """Normalize unified gateway base_url for Gemini native SSE.
+
+        Many unified gateways expose OpenAI-compat under `/v1/*`, but Gemini native endpoints
+        are usually implemented under `/v1beta/*` semantics.
+        """
+        raw = str(base_url or "").strip()
+        if not raw:
+            return raw
+        # Strip trailing slashes first.
+        while raw.endswith("/"):
+            raw = raw[:-1]
+        # Common unified gateway layout: https://host/v1  -> https://host/v1beta
+        if raw.lower().endswith("/v1"):
+            return raw[:-3] + "/v1beta"
+        # Some gateways keep an `/openai` suffix for compat endpoints.
+        if raw.lower().endswith("/openai"):
+            raw = raw[:-7]
+            if raw.lower().endswith("/v1"):
+                return raw[:-3] + "/v1beta"
+            return raw
+        return raw
+
+    def _auth_headers(self) -> dict[str, str]:
+        k = str(self.api_key or "").strip()
+        if not k:
+            return {}
+        # If user stored a bearer token in the profile secret.
+        if k.lower().startswith("bearer "):
+            return {"authorization": k}
+        # Gemini API supports x-goog-api-key; unified gateways often accept this too.
+        return {"x-goog-api-key": k}
+
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        system_instruction, contents = self._openai_messages_to_gemini_contents(messages)
+        decls = self._tool_decls_from_openai_tools(tools)
+        req: dict[str, Any] = {"contents": contents}
+        if system_instruction is not None:
+            req["systemInstruction"] = system_instruction
+        if decls:
+            req["tools"] = [{"functionDeclarations": decls}]
+        thinking_cfg = self._thinking_config_from_env()
+        if thinking_cfg is not None:
+            req["generationConfig"] = {"thinkingConfig": thinking_cfg}
+
+        url = self._build_url()
+        try:
+            import httpx
+        except Exception as exc:
+            raise RuntimeError("missing dependency: httpx") from exc
+
+        thinking_parts: list[str] = []
+        thinking_signature: str | None = None
+        out_text: list[str] = []
+        tool_calls: list[LLMToolCall] = []
+
+        def _stream_once(stream_url: str) -> None:
+            with httpx.Client(timeout=httpx.Timeout(60.0, connect=10.0)) as client:
+                with client.stream(
+                    "POST",
+                    stream_url,
+                    json=req,
+                    headers={
+                        "Content-Type": "application/json",
+                        "accept": "text/event-stream",
+                        **self._auth_headers(),
+                    },
+                ) as r:
+                    r.raise_for_status()
+                    for line in r.iter_lines():
+                        if not line or not str(line).startswith("data:"):
+                            continue
+                        data = str(line)[len("data:") :].strip()
+                        if not data or data == "[DONE]":
+                            continue
+                        try:
+                            chunk = json.loads(data)
+                        except Exception:
+                            continue
+                        cands = chunk.get("candidates") if isinstance(chunk, dict) else None
+                        if not isinstance(cands, list) or not cands:
+                            continue
+                        c0 = cands[0] if isinstance(cands[0], dict) else {}
+                        content0 = c0.get("content") if isinstance(c0.get("content"), dict) else {}
+                        parts = content0.get("parts") if isinstance(content0.get("parts"), list) else []
+                        for p in parts:
+                            if not isinstance(p, dict):
+                                continue
+                            if p.get("thought") is True:
+                                txt = str(p.get("text") or "")
+                                if txt:
+                                    thinking_parts.append(txt)
+                                sig = p.get("thoughtSignature")
+                                if isinstance(sig, str) and sig:
+                                    thinking_signature = sig
+                                continue
+                            fc = p.get("functionCall") if isinstance(p.get("functionCall"), dict) else None
+                            if fc is not None:
+                                name = str(fc.get("name") or "").strip()
+                                args = fc.get("args") if isinstance(fc.get("args"), dict) else {}
+                                tid = str(fc.get("id") or "") or f"call_{uuid.uuid4().hex}"
+                                tool_calls.append(
+                                    LLMToolCall(id=tid, name=name, arguments=args, thought_signature=thinking_signature)
+                                )
+                                continue
+                            txt = str(p.get("text") or "")
+                            if txt:
+                                out_text.append(txt)
+                                if on_token:
+                                    on_token(txt)
+
+        try:
+            _stream_once(url)
+        except Exception as exc:
+            # If gateway returns 500 on /v1* SSE path, try /v1beta* fallback once.
+            msg = str(exc or "").lower()
+            if "500" in msg and "/v1/" in str(url).lower():
+                alt = str(url).replace("/v1/", "/v1beta/")
+                _stream_once(alt)
+            else:
+                raise
+
+        return LLMResponse(
+            content="".join(out_text),
+            tool_calls=tool_calls,
+            reasoning_content="".join(thinking_parts).strip(),
+        )
+
+
+__all__ = ["GoogleGeminiChatModel"]
+

platform/llm/transports/openai_chat_completions.py

@@ -0,0 +1,309 @@
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+import uuid
+from typing import Any, Optional
+from collections.abc import Callable
+
+from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall, normalize_image_b64_payload, coerce_thought_signature_for_storage
+
+logger = logging.getLogger(__name__)
+
+
+def _likely_gemini_openai_compat_base_url(url: str | None) -> bool:
+    if not url:
+        return False
+    u = url.lower()
+    return "generativelanguage.googleapis.com" in u or "aiplatform.googleapis.com" in u
+
+
+def _model_id_suggests_gemini(model: str | None) -> bool:
+    return "gemini" in (model or "").lower()
+
+
+def _find_thought_signature_in_obj(o: Any) -> str | None:
+    if isinstance(o, dict):
+        for k in ("thought_signature", "thoughtSignature"):
+            v = o.get(k)
+            if v is not None:
+                coerced = coerce_thought_signature_for_storage(v)
+                if coerced is not None:
+                    return coerced
+        for v in o.values():
+            found = _find_thought_signature_in_obj(v)
+            if found is not None:
+                return found
+    elif isinstance(o, list):
+        for v in o:
+            found = _find_thought_signature_in_obj(v)
+            if found is not None:
+                return found
+    return None
+
+
+def _extract_thought_signature_from_message_tool_call(tc: Any) -> str | None:
+    if hasattr(tc, "model_dump"):
+        try:
+            d = tc.model_dump(mode="python")
+            ec = d.get("extra_content")
+            if isinstance(ec, dict):
+                g = ec.get("google")
+                if isinstance(g, dict) and "thought_signature" in g:
+                    return coerce_thought_signature_for_storage(g.get("thought_signature"))
+        except Exception:
+            pass
+    ec = getattr(tc, "extra_content", None)
+    if isinstance(ec, dict):
+        g = ec.get("google")
+        if isinstance(g, dict) and "thought_signature" in g:
+            return coerce_thought_signature_for_storage(g.get("thought_signature"))
+    return _extract_thought_signature_from_tool_delta(tc)
+
+
+def _extract_thought_signature_from_tool_delta(tc: Any) -> str | None:
+    for attr in ("thought_signature", "thoughtSignature"):
+        v = getattr(tc, attr, None)
+        if v is not None:
+            return coerce_thought_signature_for_storage(v)
+    extra = getattr(tc, "model_extra", None)
+    if isinstance(extra, dict):
+        for k in ("thought_signature", "thoughtSignature"):
+            ev = extra.get(k)
+            if ev is not None:
+                return coerce_thought_signature_for_storage(ev)
+    pyd_ex = getattr(tc, "__pydantic_extra__", None)
+    if isinstance(pyd_ex, dict):
+        for k in ("thought_signature", "thoughtSignature"):
+            ev = pyd_ex.get(k)
+            if ev is not None:
+                return coerce_thought_signature_for_storage(ev)
+    if hasattr(tc, "model_dump"):
+        try:
+            found = _find_thought_signature_in_obj(tc.model_dump(mode="python"))
+            if found is not None:
+                return found
+        except Exception:
+            pass
+    return None
+
+
+class OpenAIChatModel(ChatModel):
+    def __init__(
+        self,
+        model: str | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+    ):
+        self.model = model or os.getenv("OPENAI_MODEL") or "gpt-4o-mini"
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
+        self.base_url = base_url or os.getenv("OPENAI_BASE_URL")
+
+        if not self.api_key:
+            raise RuntimeError("未设置 OPENAI_API_KEY，无法使用 OpenAI 模型")
+
+        try:
+            from openai import OpenAI
+        except Exception as e:
+            raise RuntimeError("未安装 openai 依赖，请先 pip install -r requirements.txt") from e
+
+        client_kwargs: dict[str, Any] = {"api_key": self.api_key}
+        if self.base_url:
+            client_kwargs["base_url"] = self.base_url
+        self._client = OpenAI(**client_kwargs)
+        self._skip_tools: bool = False
+
+    def _create_chat_completion(self, norm_msgs: list[dict[str, Any]], tools: list[dict[str, Any]], *, stream: bool):
+        # replay_policy/tool_wire_policy live in sibling modules; import lazily to avoid cycles.
+        try:
+            from oclaw.platform.llm.replay_policy import apply_replay_policy_to_messages, resolve_replay_policy
+
+            policy = resolve_replay_policy(self.base_url, self.model)
+            if policy.enabled:
+                norm_msgs = apply_replay_policy_to_messages(norm_msgs, policy)
+        except Exception as exc:
+            logger.warning("replay_policy apply failed (%s); continuing without rewrite", exc)
+
+        use_tools = bool(tools)
+        cleaned_msgs: list[dict[str, Any]] = []
+        for m in norm_msgs or []:
+            if not isinstance(m, dict):
+                continue
+            if str(m.get("role") or "") == "tool":
+                mm = dict(m)
+                for k in ("tool_call_id", "call_id"):
+                    if k in mm:
+                        v = mm.get(k)
+                        try:
+                            sv = str(v).strip() if v is not None else ""
+                        except Exception:
+                            sv = ""
+                        if not sv:
+                            mm.pop(k, None)
+                        else:
+                            mm[k] = sv
+                cleaned_msgs.append(mm)
+            else:
+                cleaned_msgs.append(m)
+
+        kwargs: dict[str, Any] = {"model": self.model, "messages": cleaned_msgs, "stream": stream}
+        if use_tools:
+            try:
+                from oclaw.platform.config.paths import db_path
+                from oclaw.platform.persistence.sqlite_store import SqliteStore
+                from oclaw.tools.exposure_plan import build_llm_tools_plan
+
+                plan = build_llm_tools_plan(
+                    store=SqliteStore(db_path()),
+                    role="",
+                    base_url=self.base_url,
+                    max_json_bytes=_default_max_openai_tools_json_bytes(self.base_url),
+                    include_mcp=False,
+                    preview_internal=False,
+                    raw_openai_tools_override=tools,
+                )
+                kwargs["tools"] = plan.tools_wired
+            except Exception:
+                kwargs["tools"] = tools
+        return self._client.chat.completions.create(**kwargs)
+
+    def _llm_response_from_completion(self, completion: Any, *, on_token: Optional[Callable[[str], None]]) -> LLMResponse:
+        msg = completion.choices[0].message
+        reasoning_parts = getattr(msg, "reasoning_content", None) or ""
+        reasoning_text = str(reasoning_parts).strip() if reasoning_parts else ""
+        content = msg.content or ""
+        if on_token and content:
+            on_token(content)
+
+        tool_calls: list[LLMToolCall] = []
+        if msg.tool_calls:
+            for tc in msg.tool_calls:
+                tid = str(getattr(tc, "id", "") or "")
+                fn = getattr(tc, "function", None)
+                name = str(fn.name) if fn and getattr(fn, "name", None) else ""
+                args_raw = str(fn.arguments) if fn and getattr(fn, "arguments", None) is not None else "{}"
+                if not name and not tid:
+                    continue
+                if not tid:
+                    tid = f"call_{uuid.uuid4().hex}"
+                try:
+                    args = json.loads(args_raw)
+                except json.JSONDecodeError:
+                    args = {"_raw": args_raw}
+                sig = _extract_thought_signature_from_message_tool_call(tc)
+                if sig is None and fn is not None:
+                    sig = _extract_thought_signature_from_tool_delta(fn)
+                tool_calls.append(LLMToolCall(id=tid, name=name, arguments=args, thought_signature=sig))
+        return LLMResponse(content=content, tool_calls=tool_calls, reasoning_content=reasoning_text)
+
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        # Normalize input_image to image_url.
+        norm_msgs = []
+        for m in messages:
+            if m.get("role") == "user" and isinstance(m.get("content"), list):
+                norm_content = []
+                for item in m["content"]:
+                    if isinstance(item, dict) and item.get("type") == "input_image":
+                        mime = str(item.get("mime") or "image/jpeg").strip() or "image/jpeg"
+                        b64 = normalize_image_b64_payload(item.get("image_base64") or item.get("data"))
+                        if not b64:
+                            continue
+                        norm_content.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}})
+                    else:
+                        norm_content.append(item)
+                norm_msgs.append({"role": m["role"], "content": norm_content})
+            else:
+                norm_msgs.append(m)
+
+        try:
+            stream = self._create_chat_completion(norm_msgs, tools, stream=True)
+        except Exception as exc:
+            logger.info("Streaming chat completion failed; fallback to non-stream (%s)", exc)
+            completion = self._create_chat_completion(norm_msgs, tools, stream=False)
+            return self._llm_response_from_completion(completion, on_token=on_token)
+
+        content_parts: list[str] = []
+        tool_acc: dict[int, dict[str, Any]] = {}
+        reasoning_parts: list[str] = []
+
+        def _consume_stream(stream_obj: Any) -> None:
+            nonlocal content_parts, tool_acc, reasoning_parts
+            for chunk in stream_obj:
+                if not chunk.choices:
+                    continue
+                delta = chunk.choices[0].delta
+                rc = getattr(delta, "reasoning_content", None) or ""
+                if rc:
+                    reasoning_parts.append(rc)
+                if delta.tool_calls:
+                    for tc in delta.tool_calls:
+                        idx = int(tc.index)
+                        slot = tool_acc.setdefault(idx, {"id": None, "name": None, "arguments": "", "thought_signature": None})
+                        if tc.id:
+                            slot["id"] = tc.id
+                        if tc.function:
+                            if tc.function.name:
+                                slot["name"] = tc.function.name
+                            if tc.function.arguments:
+                                slot["arguments"] = f"{slot['arguments']}{tc.function.arguments}"
+                        sig = _extract_thought_signature_from_message_tool_call(tc)
+                        if sig is None:
+                            sig = _extract_thought_signature_from_tool_delta(tc)
+                        if sig is None and tc.function is not None:
+                            sig = _extract_thought_signature_from_tool_delta(tc.function)
+                        if sig is not None:
+                            slot["thought_signature"] = sig
+                if delta.content:
+                    content_parts.append(delta.content)
+                    if on_token:
+                        on_token(delta.content)
+
+        try:
+            _consume_stream(stream)
+        except Exception:
+            raise
+
+        content = "".join(content_parts)
+        reasoning_text = "".join(reasoning_parts).strip()
+
+        tool_calls: list[LLMToolCall] = []
+        for idx in sorted(tool_acc.keys()):
+            slot = tool_acc[idx]
+            tid = slot.get("id") or ""
+            name = slot.get("name") or ""
+            args_raw = slot.get("arguments") or "{}"
+            if not name and not tid:
+                continue
+            if not tid:
+                tid = f"call_{uuid.uuid4().hex}"
+            try:
+                args = json.loads(args_raw)
+            except json.JSONDecodeError:
+                args = {"_raw": args_raw}
+            ts_raw = slot.get("thought_signature")
+            tsig = coerce_thought_signature_for_storage(ts_raw) if ts_raw is not None else None
+            tool_calls.append(LLMToolCall(id=str(tid), name=str(name), arguments=args, thought_signature=tsig))
+
+        return LLMResponse(content=content, tool_calls=tool_calls, reasoning_content=reasoning_text)
+
+
+__all__ = ["OpenAIChatModel", "_likely_gemini_openai_compat_base_url", "_model_id_suggests_gemini"]
+
+
+def _default_max_openai_tools_json_bytes(base_url: str | None) -> int | None:
+    u = str(base_url or "").strip().lower()
+    if not u:
+        return None
+    # Conservative caps for some OpenAI-compatible gateways.
+    if "dashscope" in u or "aliyuncs" in u:
+        return 28_000
+    return None
+

platform/llm/transports/openai_responses.py

@@ -0,0 +1,236 @@
+from __future__ import annotations
+
+import json
+import logging
+import os
+import uuid
+from typing import Any, Optional
+from collections.abc import Callable, Iterable
+
+from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall, normalize_image_b64_payload
+
+logger = logging.getLogger(__name__)
+
+
+def _as_dict(obj: Any) -> dict[str, Any] | None:
+    if obj is None:
+        return None
+    if isinstance(obj, dict):
+        return obj
+    if hasattr(obj, "model_dump"):
+        try:
+            d = obj.model_dump(mode="python")
+            return d if isinstance(d, dict) else None
+        except Exception:
+            return None
+    return None
+
+
+def _collect_tool_calls_from_response_dict(resp: dict[str, Any]) -> list[LLMToolCall]:
+    out: list[LLMToolCall] = []
+    items = resp.get("output")
+    if not isinstance(items, list):
+        return out
+    for it in items:
+        d = _as_dict(it)
+        if not d:
+            continue
+        if str(d.get("type") or "") in ("function_call", "tool_call"):
+            call_id = str(d.get("call_id") or d.get("id") or "") or f"call_{uuid.uuid4().hex}"
+            name = str(d.get("name") or "") or str((d.get("function") or {}).get("name") or "")
+            args = d.get("arguments") or d.get("input") or {}
+            if isinstance(args, str):
+                try:
+                    args = json.loads(args)
+                except Exception:
+                    args = {"_raw": args}
+            if not isinstance(args, dict):
+                args = {"_raw": args}
+            out.append(LLMToolCall(id=call_id, name=name, arguments=args, thought_signature=None))
+    return out
+
+
+def parse_openai_responses_stream_events(
+    events: Iterable[Any],
+    *,
+    on_token: Optional[Callable[[str], None]] = None,
+) -> tuple[str, list[LLMToolCall], dict[str, Any] | None]:
+    """Pure stream parser (offline-testable).
+
+    Returns: (assembled_text, tool_calls, final_response_dict?)
+    """
+    parts: list[str] = []
+    final_resp: dict[str, Any] | None = None
+    for ev in events:
+        d = _as_dict(ev) or {}
+        typ = str(d.get("type") or "")
+        # Text deltas
+        if typ in ("response.output_text.delta", "response.output_text"):
+            delta = d.get("delta")
+            if delta is None:
+                delta = d.get("text")
+            s = str(delta or "")
+            if s:
+                parts.append(s)
+                if on_token:
+                    on_token(s)
+            continue
+        # Sometimes the SDK emits generic `response.delta` with nested segments.
+        if typ == "response.delta":
+            delta = d.get("delta")
+            if isinstance(delta, dict):
+                txt = delta.get("output_text")
+                if isinstance(txt, str) and txt:
+                    parts.append(txt)
+                    if on_token:
+                        on_token(txt)
+            continue
+        # Terminal response object
+        if typ in ("response.completed", "response.complete", "response.done"):
+            resp = d.get("response") or d.get("data") or d.get("result")
+            final_resp = _as_dict(resp) or final_resp
+            continue
+        if typ == "response.created":
+            continue
+        # Some SDK versions yield the response object directly (no explicit event type).
+        if d.get("output") is not None and d.get("id") is not None:
+            final_resp = d
+
+    text = "".join(parts)
+    tool_calls = _collect_tool_calls_from_response_dict(final_resp) if final_resp else []
+    return text, tool_calls, final_resp
+
+
+class OpenAIResponsesModel(ChatModel):
+    """OpenAI Responses API transport (OpenAI-compatible gateways may implement this surface)."""
+
+    def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
+        self.model = (model or os.getenv("OPENAI_MODEL") or "gpt-4o-mini").strip()
+        self.api_key = (api_key or os.getenv("OPENAI_API_KEY") or "").strip()
+        self.base_url = (base_url or os.getenv("OPENAI_BASE_URL") or "").strip() or None
+        if not self.api_key:
+            raise RuntimeError("未设置 OPENAI_API_KEY，无法使用 OpenAI Responses")
+        try:
+            from openai import OpenAI
+        except Exception as e:
+            raise RuntimeError("未安装 openai 依赖，请先 pip install -r requirements.txt") from e
+        kw: dict[str, Any] = {"api_key": self.api_key}
+        if self.base_url:
+            kw["base_url"] = self.base_url
+        self._client = OpenAI(**kw)
+
+    @staticmethod
+    def _normalize_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        """
+        Normalize to the strictest OpenAI-compatible `responses` shape:
+        - input.messages[*].role MUST be "user"
+        - input.messages[*].content MUST be a list (content blocks)
+        - text blocks use {"type":"text","text":...}
+        - image blocks use {"type":"image_url","image_url":{"url":"..."}} or {"type":"input_image","image_url":"..."} depending on gateway;
+          we prefer the common "image_url" block here.
+        """
+        out: list[dict[str, Any]] = []
+        for m in messages or []:
+            if not isinstance(m, dict):
+                continue
+            role = str(m.get("role") or "user").strip().lower() or "user"
+            content = m.get("content")
+            norm_content: list[dict[str, Any]] = []
+            if isinstance(content, list):
+                for item in content:
+                    if isinstance(item, dict) and item.get("type") == "input_image":
+                        mime = str(item.get("mime") or "image/jpeg").strip() or "image/jpeg"
+                        b64 = normalize_image_b64_payload(item.get("image_base64") or item.get("data"))
+                        if not b64:
+                            continue
+                        norm_content.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}})
+                        continue
+                    if isinstance(item, dict) and item.get("type") in ("input_text", "text"):
+                        t = str(item.get("text") or "").strip()
+                        if t:
+                            norm_content.append({"type": "text", "text": t})
+                        continue
+                    if isinstance(item, dict):
+                        # Best-effort coerce unknown blocks into valid text blocks.
+                        s = str(item.get("text") or "").strip()
+                        if s:
+                            norm_content.append({"type": "text", "text": s})
+                        continue
+                    s = str(item or "").strip()
+                    if s:
+                        norm_content.append({"type": "text", "text": s})
+            elif isinstance(content, str):
+                txt = content.strip()
+                if txt:
+                    norm_content.append({"type": "text", "text": txt})
+            elif content is not None:
+                s = str(content).strip()
+                if s:
+                    norm_content.append({"type": "text", "text": s})
+            if not norm_content:
+                # Keep shape valid even for empty messages.
+                continue
+
+            # Enforce role=user and preserve other roles via prefix tag.
+            if role != "user":
+                prefix = "assistant" if role == "assistant" else ("system" if role == "system" else role)
+                norm_content.insert(0, {"type": "text", "text": f"[{prefix}]"})
+            out.append({"role": "user", "content": norm_content})
+        return out
+
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        norm = self._normalize_messages(messages)
+        # OpenAI-compatible gateways differ: some require input={"messages":[...]} with role=user only.
+        stream_errors: list[str] = []
+        stream_variants: list[dict[str, Any]] = [
+            {"model": self.model, "input": {"messages": norm}, "tools": tools or None, "stream": True},
+            {"model": self.model, "input": norm, "tools": tools or None, "stream": True},
+        ]
+        try:
+            for payload in stream_variants:
+                try:
+                    stream = self._client.responses.create(**payload)
+                    text, tool_calls, final_resp = parse_openai_responses_stream_events(stream, on_token=on_token)
+                    if (not text.strip()) and final_resp:
+                        ot = final_resp.get("output_text")
+                        if isinstance(ot, str) and ot.strip():
+                            text = ot
+                            if on_token:
+                                on_token(text)
+                    return LLMResponse(content=text, tool_calls=tool_calls)
+                except Exception as exc:
+                    stream_errors.append(str(exc))
+                    continue
+            raise RuntimeError("; ".join(stream_errors) or "responses_stream_all_variants_failed")
+        except Exception as exc:
+            logger.info("responses stream failed; fallback to non-stream (%s)", exc)
+            nonstream_errors: list[str] = []
+            for payload in (
+                {"model": self.model, "input": {"messages": norm}, "tools": tools or None},
+                {"model": self.model, "input": norm, "tools": tools or None},
+            ):
+                try:
+                    resp = self._client.responses.create(**payload)
+                    d = _as_dict(resp) or {}
+                    text = str(d.get("output_text") or "")
+                    tool_calls = _collect_tool_calls_from_response_dict(d)
+                    if on_token and text:
+                        on_token(text)
+                    return LLMResponse(content=text, tool_calls=tool_calls)
+                except Exception as e2:
+                    nonstream_errors.append(str(e2))
+                    continue
+            raise RuntimeError(
+                "openai_responses_request_failed: "
+                + " | ".join([str(exc)] + nonstream_errors[-2:])
+            ) from exc
+
+
+__all__ = ["OpenAIResponsesModel", "parse_openai_responses_stream_events"]
+

platform/llm/transports/simple.py

@@ -0,0 +1,144 @@
+from __future__ import annotations
+
+import json
+import re
+from typing import Any, Optional
+from collections.abc import Callable
+
+from oclaw.platform.llm.transports.base import ChatModel, LLMResponse, LLMToolCall
+
+
+class StaticTextChatModel(ChatModel):
+    def __init__(self, text: str):
+        self._text = text or ""
+
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        if on_token and self._text:
+            on_token(self._text)
+        return LLMResponse(content=self._text, tool_calls=[])
+
+
+class RuleBasedChatModel(ChatModel):
+    _IP_RE = re.compile(
+        r"(?P<ip>(?:\d{1,3}\.){3}\d{1,3})|(?P<ipv6>(?:[0-9a-fA-F]{0,4}:){2,}[0-9a-fA-F]{0,4})"
+    )
+
+    def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        *,
+        on_token: Optional[Callable[[str], None]] = None,
+    ) -> LLMResponse:
+        def make_call(name: str, arguments: dict[str, Any]) -> LLMResponse:
+            return LLMResponse(
+                content="",
+                tool_calls=[LLMToolCall(id=f"rb_{name}_1", name=name, arguments=arguments)],
+            )
+
+        def emit(resp: LLMResponse) -> LLMResponse:
+            if on_token and (resp.content or "").strip():
+                on_token(resp.content)
+            return resp
+
+        if messages:
+            last = messages[-1]
+            if last.get("role") == "tool":
+                raw = str(last.get("content") or "")
+                try:
+                    data = json.loads(raw)
+                except json.JSONDecodeError:
+                    data = {"ok": True, "raw": raw}
+
+                if isinstance(data, dict) and data.get("ok") is False:
+                    return emit(LLMResponse(content=f"工具执行失败：{data.get('error')}", tool_calls=[]))
+
+                if isinstance(data, dict) and "route" in data:
+                    r = data.get("route") or {}
+                    return emit(
+                        LLMResponse(
+                            content=f"路由查询结果：目的 {data.get('destination')} 命中 {r.get('prefix')}，下一跳 {r.get('next_hop')}，出接口 {r.get('out_if')}。",
+                            tool_calls=[],
+                        )
+                    )
+
+                if isinstance(data, dict) and "hops" in data:
+                    hops = data.get("hops") or []
+                    return emit(
+                        LLMResponse(
+                            content=f"路径计算结果：{data.get('src')} → {data.get('dst')} 共 {data.get('hop_count')} 跳，路径为：{' -> '.join(map(str, hops))}",
+                            tool_calls=[],
+                        )
+                    )
+
+                if isinstance(data, dict) and "diff" in data:
+                    diff = data.get("diff") or ""
+                    if not diff:
+                        return emit(LLMResponse(content="配置对比结果：两段配置一致。", tool_calls=[]))
+                    return emit(LLMResponse(content=f"配置对比结果：\n\n```diff\n{diff}\n```", tool_calls=[]))
+
+                if isinstance(data, dict) and "level_count" in data:
+                    lc = data.get("level_count") or {}
+                    return emit(LLMResponse(content=f"日志分析结果：统计 {lc}。建议优先查看 ERROR 样例与日志尾部定位根因。", tool_calls=[]))
+
+                if isinstance(data, dict) and "reachable" in data:
+                    reach = bool(data.get("reachable"))
+                    avg = data.get("avg_ms")
+                    if reach:
+                        tail = f"平均时延 {avg}ms" if isinstance(avg, int) else "时延解析失败"
+                        return emit(LLMResponse(content=f"设备状态：可达，{tail}。", tool_calls=[]))
+                    return emit(LLMResponse(content="设备状态：不可达。建议检查 ACL/路由/DNS/防火墙策略，并确认目标在线。", tool_calls=[]))
+
+                return emit(LLMResponse(content=f"工具执行结果：{json.dumps(data, ensure_ascii=False)}", tool_calls=[]))
+
+        last_user = ""
+        for m in reversed(messages):
+            if m.get("role") == "user":
+                content = m.get("content")
+                if isinstance(content, list):
+                    texts = [item["text"] for item in content if isinstance(item, dict) and item.get("type") == "text"]
+                    last_user = "\n".join(texts)
+                else:
+                    last_user = str(content or "")
+                break
+
+        m = self._IP_RE.search(last_user)
+        if "路由" in last_user and m:
+            ip = m.group("ip") or m.group("ipv6")
+            return make_call("query_route", {"destination": ip})
+
+        if ("路径" in last_user or "链路" in last_user) and ("到" in last_user or "->" in last_user):
+            m2 = re.search(r"([A-Za-z0-9_-]+)\s*(?:到|->)\s*([A-Za-z0-9_-]+)", last_user)
+            if m2:
+                src, dst = m2.group(1), m2.group(2)
+                return make_call("get_path", {"src": src, "dst": dst})
+
+        if "对比" in last_user or "diff" in last_user.lower():
+            if "left:" in last_user.lower() and "right:" in last_user.lower():
+                left = last_user.split("left:", 1)[1].split("right:", 1)[0].strip()
+                right = last_user.split("right:", 1)[1].strip()
+                return make_call("config_diff", {"left": left, "right": right})
+
+        if "日志" in last_user and ("分析" in last_user or "统计" in last_user):
+            return make_call("log_analysis", {"log": last_user})
+
+        if ("设备" in last_user or "主机" in last_user) and ("状态" in last_user or "可达" in last_user or "ping" in last_user.lower()) and m:
+            ip = m.group("ip") or m.group("ipv6")
+            return make_call("device_status", {"host": ip})
+
+        return emit(
+            LLMResponse(
+                content="我可以进行路由查询、路径计算、配置对比、设备状态检查、日志分析。请直接描述需求并提供必要输入。",
+                tool_calls=[],
+            )
+        )
+
+
+__all__ = ["StaticTextChatModel", "RuleBasedChatModel"]
+