"""Run a minimal actual Mem0 smoke test with Gemini via OpenRouter. This is intentionally a smoke test, not a benchmark. It verifies that the public Mem0 codebase can execute in this environment with: * OpenRouter/Gemini as the LLM backend; * local HuggingFace embeddings; * local Qdrant storage. The script writes JSON outputs under ``llm_memory_validation/mem0_actual_smoke`` and never prints API keys. """ from __future__ import annotations import argparse import json import os import shutil from pathlib import Path from typing import Any DEFAULT_MODEL = "google/gemini-3.1-flash-lite-preview" def load_env_file(path: Path) -> None: if not path.exists(): return for line in path.read_text(encoding="utf-8").splitlines(): stripped = line.strip() if not stripped or stripped.startswith("#") or "=" not in stripped: continue key, value = stripped.split("=", 1) os.environ.setdefault(key.strip(), value.strip().strip('"').strip("'")) def build_config(out_dir: Path, model: str) -> dict[str, Any]: return { "llm": { "provider": "openai", "config": { "model": model, "temperature": 0.0, "max_tokens": 700, "openrouter_base_url": "https://openrouter.ai/api/v1", "site_url": "https://localhost/oraclemem", "app_name": "OracleMem Mem0 Baseline Smoke", }, }, "embedder": { "provider": "huggingface", "config": {"model": "multi-qa-MiniLM-L6-cos-v1"}, }, "vector_store": { "provider": "qdrant", "config": { "collection_name": "oraclemem_mem0_smoke", "path": str(out_dir / "qdrant"), "embedding_model_dims": 384, }, }, "history_db_path": str(out_dir / "history.db"), "version": "v1.1", } def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--api-env", type=Path, default=Path("api.env")) parser.add_argument("--out-dir", type=Path, default=Path("llm_memory_validation/mem0_actual_smoke")) parser.add_argument("--model", default=DEFAULT_MODEL) parser.add_argument("--reuse-store", action="store_true") args = parser.parse_args() load_env_file(args.api_env) if not os.environ.get("OPENROUTER_API_KEY"): raise RuntimeError("OPENROUTER_API_KEY is required in the environment or api.env") os.environ.setdefault("MEM0_TELEMETRY", "false") os.environ.setdefault("USE_TF", "0") os.environ.setdefault("TRANSFORMERS_NO_TF", "1") os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3") if args.out_dir.exists() and not args.reuse_store: shutil.rmtree(args.out_dir) args.out_dir.mkdir(parents=True, exist_ok=True) from mem0 import Memory config = build_config(args.out_dir, args.model) status: dict[str, Any] = {"ok": False, "stage": "init", "model": args.model} try: memory = Memory.from_config(config) if not args.reuse_store: status["stage"] = "add" add_result = memory.add( [ {"role": "user", "content": "I moved to Seattle last month. I prefer vegetarian restaurants."}, {"role": "assistant", "content": "Noted."}, {"role": "user", "content": "Actually, I now live in Portland, but I still prefer vegetarian food."}, ], user_id="oraclemem_smoke", ) else: add_result = {"skipped": True} filters = {"user_id": "oraclemem_smoke"} status["stage"] = "get_all" all_result = memory.get_all(filters=filters, top_k=20) status["stage"] = "search" search_result = memory.search( query="Where do I live now and what food do I prefer?", filters=filters, top_k=5, ) status.update( { "ok": True, "stage": "done", "add_result": add_result, "all_result": all_result, "search_result": search_result, } ) except Exception as exc: status.update({"ok": False, "error_type": type(exc).__name__, "error": str(exc)}) (args.out_dir / "search_result.json").write_text(json.dumps(status, indent=2, default=str), encoding="utf-8") print(json.dumps({k: status[k] for k in status if k not in {"add_result", "all_result", "search_result"}}, indent=2)) if not status["ok"]: raise SystemExit(1) if __name__ == "__main__": main()