"""Upload a Hermes session transcript to Hugging Face as an agent trace, re-emitted in the **Claude Code JSONL** shape the HF Agent Trace Viewer auto-detects (https://huggingface.co/docs/hub/agent-traces). Deterministic, zero LLM turns. Private by default: traces can carry prompts, tool output, local paths and secrets, so the dataset is created private and every text body passes the secret redactor (``force=True``) unless ``redact=False``. :func:`upload_session_trace` never raises (returns a user-facing status string); programmatic callers use :func:`build_trace_jsonl` + :func:`_do_upload`.""" from __future__ import annotations from pm import install_hint import json import logging import os import uuid from datetime import datetime, timezone from typing import Any, Dict, List, Optional, Tuple logger = logging.getLogger(__name__) DEFAULT_DATASET_NAME = "hermes-traces" _HERMES_VERSION = "hermes-agent" _REDACTION_BLOCKED_MESSAGE = ( "Trace upload blocked: secret redaction failed, so the transcript may " "still contain credentials or other sensitive data. Fix the redactor or " "rerun with --no-redact only after manually reviewing the transcript." ) _NO_TOKEN_MESSAGE = ( "Can't upload — no Hugging Face token is available. To set it up:\n" "\n" "1. Create a token with WRITE access at https://huggingface.co/settings/tokens\n" " (New token -> type \"Write\" -> copy it).\n" "2. Add it to your environment as HF_TOKEN (e.g. in ~/.hermes/.env):\n" " HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxx\n" "3. Run /upload-trace again (or `hermes trace upload`)." ) _TOKEN_ENV_VARS = ("HF_TOKEN", "HUGGINGFACE_HUB_TOKEN", "HUGGING_FACE_HUB_TOKEN", "HUGGINGFACE_TOKEN") class TraceRedactionError(RuntimeError): """Raised when a trace cannot be safely redacted before upload.""" # --- Conversion: Hermes OpenAI-format messages -> Claude Code JSONL --- def _now_iso() -> str: return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3] + "Z" def _redact(text: Any, enabled: bool) -> Any: """Redact a string body when enabled (``force=True``: an upload scrubs even if log redaction is off).""" if not enabled or not isinstance(text, str) or not text: return text try: from agent.redact import redact_sensitive_text return redact_sensitive_text(text, force=True) except Exception as exc: logger.warning("Trace upload redaction failed; refusing upload", exc_info=True) raise TraceRedactionError(_REDACTION_BLOCKED_MESSAGE) from exc def _text_block(text: Any, redact: bool) -> Dict[str, Any]: return {"type": "text", "text": _redact(text, redact)} def _part_to_block(part: Any, redact: bool) -> Dict[str, Any]: if not isinstance(part, dict): return _text_block(str(part), redact) if part.get("type") == "text": return _text_block(part.get("text", ""), redact) if part.get("type") in ("image_url", "image"): return {"type": "text", "text": "[image omitted]"} # the viewer renders text turns; no base64 return _text_block(json.dumps(part), redact) def _content_to_blocks(content: Any, redact: bool) -> List[Dict[str, Any]]: """Normalize a message ``content`` field into Anthropic content blocks.""" if isinstance(content, list): return [_part_to_block(part, redact) for part in content] return [] if content is None else [_text_block(content if isinstance(content, str) else json.dumps(content), redact)] def _parse_tool_args(raw_args: Any) -> Dict[str, Any]: if not isinstance(raw_args, str): return raw_args if isinstance(raw_args, dict) else {} try: return json.loads(raw_args) if raw_args.strip() else {} except (json.JSONDecodeError, ValueError): return {"_raw": raw_args} def _tool_calls_to_blocks(tool_calls: Any, redact: bool) -> List[Dict[str, Any]]: """Convert OpenAI tool_calls into Anthropic ``tool_use`` content blocks.""" blocks: List[Dict[str, Any]] = [] for tc in tool_calls if isinstance(tool_calls, list) else (): if not isinstance(tc, dict): continue fn = tc.get("function") or {} parsed = _parse_tool_args(fn.get("arguments")) if redact: try: parsed = json.loads(_redact(json.dumps(parsed), redact)) except (json.JSONDecodeError, ValueError): logger.warning("Trace upload redacted tool arguments are not valid JSON; refusing upload") raise TraceRedactionError(_REDACTION_BLOCKED_MESSAGE) blocks.append({"type": "tool_use", "id": tc.get("id") or f"toolu_{uuid.uuid4().hex[:16]}", "name": fn.get("name") or tc.get("name") or "tool", "input": parsed}) return blocks def _git_branch(cwd: str) -> str: if not cwd: return "" try: import subprocess r = subprocess.run(["git", "rev-parse", "--abbrev-ref", "HEAD"], capture_output=True, text=True, encoding="utf-8", errors="replace", timeout=3, cwd=cwd, stdin=subprocess.DEVNULL) except Exception: return "" return r.stdout.strip() if r.returncode == 0 else "" def _assistant_message(msg: Dict[str, Any], model: str, redact: bool) -> Dict[str, Any]: blocks = _content_to_blocks(msg.get("content"), redact) + _tool_calls_to_blocks(msg.get("tool_calls"), redact) return {"role": "assistant", "model": model or "unknown", "content": blocks or [{"type": "text", "text": ""}]} def _tool_result_message(msg: Dict[str, Any], model: str, redact: bool) -> Dict[str, Any]: content = msg.get("content") return {"role": "user", "content": [{ "type": "tool_result", "tool_use_id": msg.get("tool_call_id") or msg.get("tool_name") or "tool", "content": _redact(content if isinstance(content, str) else json.dumps(content), redact), }]} def _user_message(msg: Dict[str, Any], model: str, redact: bool) -> Dict[str, Any]: content = msg.get("content") return {"role": "user", "content": _redact(content, redact) if isinstance(content, str) else _content_to_blocks(content, redact)} # role -> (Claude Code line type, message builder). Unknown roles render as user. _ROLE_RENDERERS: Dict[Any, Tuple[str, Any]] = {"assistant": ("assistant", _assistant_message), "tool": ("user", _tool_result_message)} def build_trace_jsonl(messages: List[Dict[str, Any]], *, session_id: str, model: str = "", cwd: str = "", redact: bool = True) -> str: """One JSONL line per non-system message: ``user``/``tool`` -> type user (tool results ride on user turns as ``tool_result`` keyed by ``tool_call_id``), ``assistant`` -> text + ``tool_use`` blocks; turns link via ``parentUuid``.""" lines: List[str] = [] parent: Optional[str] = None base_ts = _now_iso() git_branch = _git_branch(cwd) for msg in messages: role = msg.get("role") if role == "system": continue turn_uuid = str(uuid.uuid4()) line_type, render = _ROLE_RENDERERS.get(role, ("user", _user_message)) entry = { # key order is the wire order "parentUuid": parent, "isSidechain": False, "userType": "external", "cwd": cwd or os.getcwd(), "sessionId": session_id, "version": _HERMES_VERSION, "gitBranch": git_branch, "uuid": turn_uuid, "timestamp": base_ts, "type": line_type, "message": render(msg, model, redact), } lines.append(json.dumps(entry, ensure_ascii=False)) parent = turn_uuid return "\n".join(lines) + ("\n" if lines else "") # --- Upload --- def _resolve_hf_token() -> Optional[str]: """Return the user's Hugging Face token from the usual env vars.""" return next((val for var in _TOKEN_ENV_VARS if (val := (os.getenv(var) or "").strip())), None) def _do_upload( jsonl: str, *, token: str, session_id: str, dataset_name: str = DEFAULT_DATASET_NAME, private: bool = True, ) -> str: """Create (idempotently) the private dataset and push the trace file. Returns a user-facing status string. Never raises. """ try: import pm pm.ensure_import("trace-upload") except Exception: # lazy-install unavailable — fall through to the import, which # surfaces the install hint below if the package is missing. pass try: from huggingface_hub import HfApi except ImportError: return ("Hugging Face upload needs the `huggingface_hub` package. Run: " f"{install_hint('trace-upload')}") api = HfApi(token=token) try: who = api.whoami() except Exception as e: logger.warning("HF whoami failed: %s", e) return "Your Hugging Face token was rejected (whoami failed). Make sure it has WRITE access and isn't expired." user = who.get("name") if isinstance(who, dict) else None if not user: return "Could not resolve your Hugging Face username from the token." repo_id = f"{user}/{dataset_name}" try: api.create_repo(repo_id=repo_id, repo_type="dataset", private=private, exist_ok=True) except Exception as e: logger.warning("HF create_repo failed for %s: %s", repo_id, e) return f"Could not create/access dataset {repo_id}: {e}" path_in_repo = f"sessions/{session_id}.jsonl" try: api.upload_file(path_or_fileobj=jsonl.encode("utf-8"), path_in_repo=path_in_repo, repo_id=repo_id, repo_type="dataset", commit_message=f"add session trace {session_id}") except Exception as e: logger.warning("HF upload_file failed for %s: %s", repo_id, e) return f"Upload to Hugging Face failed: {e}" return (f"Uploaded -> https://huggingface.co/datasets/{repo_id}/blob/main/{path_in_repo}\n" f"View in the trace viewer: https://huggingface.co/datasets/{repo_id}") def load_session_messages(session_id: str, db_path=None) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: """``(messages, meta)`` from SQLite; ``meta`` is ``{}`` when the session row is missing (a live, untitled session may still have messages).""" from hermes_state_registry import acquire, release_or_close db = acquire(db_path or None) try: resolved = db.resolve_session_id(session_id) or session_id meta = db.get_session(resolved) or {} return db.get_messages_as_conversation(resolved), meta finally: release_or_close(db) def upload_session_trace( session_id: str, *, model: str = "", cwd: str = "", redact: bool = True, private: bool = True, dataset_name: str = DEFAULT_DATASET_NAME, db_path=None, token: Optional[str] = None, ) -> str: """CLI/gateway entry point: load, convert, upload to ``{user}/hermes-traces``. Status string, never raises.""" if not session_id: return "No active session to upload." token = token or _resolve_hf_token() if not token: return _NO_TOKEN_MESSAGE try: messages, meta = load_session_messages(session_id, db_path=db_path) except Exception as e: logger.warning("Failed to load session %s for trace upload: %s", session_id, e) return f"Could not load session {session_id}: {e}" if not messages: return "No transcript to upload for this session yet." try: jsonl = build_trace_jsonl(messages, session_id=session_id, model=model or meta.get("model") or "", cwd=cwd, redact=redact) except TraceRedactionError: return _REDACTION_BLOCKED_MESSAGE if not jsonl.strip(): return "No transcript content to upload for this session." return _do_upload(jsonl, token=token, session_id=session_id, dataset_name=dataset_name, private=private)