Odysseys: Benchmarking Web Agents on Realistic Long-Horizon Tasks

# odyssey_agent.py
import os
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.memory import ConversationBufferMemory
from playwright.sync_api import sync_playwright
import httpx
import pandas as pd
import pulp

# ------------------- Tool definitions -------------------
def scrape_prices(url: str) -> str:
    """Return a CSV string of product name, price extracted from the page."""
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        page.goto(url, timeout=30_000)
        rows = page.query_selector_all("table.price tr")
        data = []
        for r in rows[1:]:
            cells = r.query_selector_all("td")
            data.append([c.inner_text().strip() for c in cells])
        browser.close()
    df = pd.DataFrame(data, columns=["product", "price"])
    return df.to_csv(index=False)

def query_alibaba(product: str) -> str:
    """Call Alibaba API, return JSON string of suppliers."""
    api_key = os.getenv("ALIBABA_KEY")
    resp = httpx.get(
        f"https://api.alibaba.com/v1/search?q={product}",
        headers={"Authorization": f"Bearer {api_key}"},
        timeout=10,
    )
    resp.raise_for_status()
    return resp.text

def solve_budget(cpa_target: float) -> str:
    """Linear program to allocate $10k across Google and FB."""
    prob = pulp.LpProblem("AdBudget", pulp.LpMaximize)
    g, f = pulp.LpVariable("Google", lowBound=0), pulp.LpVariable("Facebook", lowBound=0)
    prob += 0.8 * g + 0.6 * f, "EstimatedRevenue"
    prob += g + f <= 10_000, "BudgetCap"
    prob += 0.05 * g + 0.07 * f <= cpa_target, "CPAConstraint"
    prob.solve()
    return f"Google=${g.value():.2f}, Facebook=${f.value():.2f}"

# ------------------- LangChain agent -------------------
tools = [
    Tool(name="ScrapePrices", func=scrape_prices, description="Scrape a competitor price table."),
    Tool(name="AlibabaSearch", func=query_alibaba, description="Search Alibaba for suppliers."),
    Tool(name="AllocateBudget", func=solve_budget, description="Compute optimal ad spend."),
]

memory = ConversationBufferMemory(memory_key="chat_history")
llm = OpenAI(temperature=0.0, model_name="gpt-4o-mini")
agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True,
    memory=memory,
)

if __name__ == "__main__":
    # Example high-level prompt that starts the Odyssey
    task = """You are launching a mini-e-commerce campaign for a bamboo toothbrush.
    Follow the 5-step plan described in the benchmark documentation."""
    print(agent.run(task))

# retry_wrapper.py
import time
from functools import wraps
import httpx

def retry_on_exception(max_retries=3, backoff=2):
    def decorator(fn):
        @wraps(fn)
        def wrapper(*args, **kwargs):
            for attempt in range(1, max_retries + 1):
                try:
                    return fn(*args, **kwargs)
                except (httpx.HTTPStatusError, httpx.ConnectTimeout) as e:
                    if attempt == max_retries:
                        raise
                    wait = backoff ** attempt
                    print(f"[Retry {attempt}/{max_retries}] {e}; sleeping {wait}s")
                    time.sleep(wait)
        return wrapper
    return decorator

# Apply to the httpx call
@retry_on_exception(max_retries=5, backoff=3)
def query_alibaba(product: str) -> str:
    # same body as before
    ...

python
# odyssey_eval.py
import json, pathlib, pandas as pd, numpy as np, subprocess, sys, time

GROUND_TRUTH = pathlib.Path("ground_truth.json")
RESULTS_DIR = pathlib.Path("agent_runs/")   # each run writes a JSON report

def load_gt():
    return json.loads(GROUND_TRUTH.read_text())

def load_run(run_path):
    return json.

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