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Agent Trace Anomaly Detection — FastAPI Backend
This is the API layer that wraps the ML pipeline built in scripts/.
All model training, feature extraction, and inference logic lives
in the partner's code (scripts/inference.py). This file just serves it.
Run from the OffRails project root:
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
Interactive docs:
http://localhost:8000/docs
"""
from __future__ import annotations
import os
import sys
import logging
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from app.api.routes import _state
# ── Make partner's scripts/ importable ───────────────────────────────────────
# inference.py does `from model import ...` and `from build_features import ...`
# so we need scripts/ on sys.path.
SCRIPTS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "scripts")
if SCRIPTS_DIR not in sys.path:
sys.path.insert(0, SCRIPTS_DIR)
from app.api.routes import router
# ── Logging ──────────────────────────────────────────────────────────────────
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)-8s %(name)s — %(message)s",
datefmt="%H:%M:%S",
)
logger = logging.getLogger(__name__)
# ── App ──────────────────────────────────────────────────────────────────────
@asynccontextmanager
async def lifespan(app: FastAPI):
from huggingface_hub import hf_hub_download
from scripts.inference import TraceAnomalyDetector
try:
model_path = hf_hub_download(
repo_id="mg643/offrails-models",
filename="xgboost_model.joblib",
)
_state["detector"] = TraceAnomalyDetector(
model_dir=os.path.dirname(model_path),
model_type="xgboost"
)
_state["model_type"] = "xgboost"
logger.info("XGBoost model loaded from HF Hub")
except Exception as e:
logger.warning(f"Could not load model: {e}")
yield
app = FastAPI(
title="Agent Trace Anomaly Detection API",
lifespan=lifespan,
description=(
"Detects anomalous agent execution traces — unnecessary tool calls, "
"circular reasoning, and goal drift.\n\n"
"**ML models** (XGBoost, DistilBERT) are trained via the pipeline in `scripts/`.\n"
"**This API** serves predictions from those trained models.\n\n"
"## Workflow\n"
"1. Train models: `python setup.py` (or `POST /pipeline/train`)\n"
"2. Load a model: `POST /models/load`\n"
"3. Predict: `POST /predict`\n"
"4. Compare models: `POST /predict/compare`\n"
),
version="1.0.0",
)
# Allow Gradio / any frontend to call the API
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(router)
# ── Root ─────────────────────────────────────────────────────────────────────
@app.get("/", include_in_schema=False)
def root():
return {
"service": "Agent Trace Anomaly Detection API",
"docs": "/docs",
"workflow": [
"1. Train models: python setup.py",
"2. POST /models/load (load xgboost or distilbert)",
"3. POST /predict (classify a trace)",
"4. POST /predict/compare (run both models)",
],
}
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