What is E-Commerce Bench
E-Commerce Bench is a long-horizon e-commerce operations evaluation benchmark open-sourced by Alibaba Tongyi Qianwen in collaboration with the Taotian Group. E-Commerce Bench lets an LLM Agent operate an online store for 365 days in a simulated real market with 100,000 yuan in capital, covering product selection, supplier negotiation, pricing, inventory, and cash flow management. The environment is based on real platform data, includes 576 suppliers (including fraudulent parties), 8 promotions, and 10 market events, and uses a deterministic kernel to ensure reproducibility, evaluating models' business operations capabilities across seven dimensions including profitability, negotiation, anti-fraud, and long-horizon learning.
Main Features of E-Commerce Bench
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Long-Horizon Operations Simulation: Lets an LLM Agent continuously operate an online store for 365 days in a dynamic market with 100,000 yuan in capital, maximizing total assets at year-end.
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Multi-Store Operations: Supports simultaneously opening up to 4 stores of 12 different types, enabling differentiated category layouts.
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Supplier Negotiation: Conducts multi-round price negotiations with 576 suppliers to secure lower procurement costs.
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Anti-Fraud Identification: Identifies and avoids 5 typical scams from 152 fraudulent suppliers (VIP fees, out of stock, quality reduction, false urgency, discount traps).
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Dynamic Market Response: Adjusts business strategies under 8 promotions and 10 unexpected events (disasters, supply chain shocks).
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Inventory and Warehousing Management: Manages the purchase, sales, and inventory of 6,886 SKUs, bearing warehousing fees calculated per item per day.
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Cash Flow Control: Handles a three-account settlement system and 9-day delayed payment collection, avoiding profitable-but-bankrupt.
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Seven-Dimensional Comprehensive Evaluation: Scores across seven independent dimensions: profitability, negotiation, anti-fraud, solvency, efficiency, execution, and long-horizon learning.
Technical Principles of E-Commerce Bench
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Four-Layer Architecture: The system is built top-down from the Agent loop layer, tool layer, environment layer, and data layer, forming a closed loop of decision-making, execution, and environmental feedback.
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Deterministic Negotiation Kernel: All supplier quotes, concessions, and accept/reject decisions are computed by a fixed algorithm kernel, and the LLM is only responsible for rendering numerical decisions into natural language dialogue. This preserves the realistic feel of multi-round haggling while completely eliminating sampling randomness, preventing models from obtaining below-cost prices through jailbreaking or deception, and ensuring reproducible results for different Agents under the same strategy.
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Dynamic Economic Engine: User demand is not randomly sampled; it is calculated from factors such as category base demand, price elasticity, market events, and store reputation, with reproducible randomness controlled only by the episode seed.
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Three-Account Settlement and Cash Flow Pressure: Costs are deducted from the bank account on the same day they occur, but sales revenue must go through "shipping → 9-day platform escrow period → platform wallet → manual withdrawal" before returning to the bank, deliberately creating a working capital gap and forcing the Agent to actively manage liquidity, avoiding short-sighted behavior of looking only at profit and not at cash.
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Long-Horizon Learning Evaluation (AnchorRatio): Measures whether an Agent gets cheaper the more it repeatedly purchases the same product. Using a random reshuffle of the Agent's own historical quotes as the null hypothesis, if it is significantly below 1.0, it indicates that the model can continuously optimize its procurement strategy from past negotiation experience.
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How to use E-Commerce Bench
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Get the code: Clone the E-Commerce Bench open-source repository from GitHub to your local machine.
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Environment setup: Install the project dependencies and prepare the Python runtime environment.
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Connect the model: Fill in the API Key and model name of the LLM to be evaluated in the configuration file.
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Start the simulation: Run the main program, and the Agent will automatically begin a 365-day e-commerce operation round.
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View the results: After the simulation ends, the system automatically outputs a seven-dimensional evaluation report and a full-year asset change curve.
Core advantages of E-Commerce Bench
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First long-horizon business operation evaluation: For the first time, LLM Agent evaluation is extended from short-horizon tasks to a dynamic business scenario of 365 days of continuous operation.
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Driven by real market data: 6,886 products, 60 categories, and 576 suppliers are all built based on anonymized data from real e-commerce platforms.
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Deterministic core ensures reproducibility: Negotiation quotes and market demand are calculated by a fixed algorithm core, while the LLM is only responsible for dialogue rendering, completely eliminating random sampling noise.
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Seven-dimensional evaluation system: It not only looks at year-end assets, but also independently assesses negotiation, anti-fraud, cash flow, efficiency, execution, and long-horizon learning capabilities.
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Realistic business pressure simulation: The three-account delayed settlement and daily warehousing fee mechanism force the Agent to manage working capital rather than merely pursue paper profits.
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Quantified anti-fraud capability: It includes 152 fraudulent suppliers and 5 typical scams, precisely measuring the model's risk identification and capital protection capabilities.
Project address of E-Commerce Bench
- Project website：https://ecbench.github.io/
- GitHub repository：https://github.com/QwenLM/E-CommerceBench
- arXiv technical paper：https://arxiv.org/pdf/2608.30730
Comparison of E-Commerce Bench with similar competitors
| Comparison dimension | E-Commerce Bench | TERMS-Bench |
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| Core positioning | Full-chain evaluation of long-horizon autonomous e-commerce operations | Specialized evaluation of multi-turn dialogue negotiation strategies |
| Task duration | 365 days of continuous operation, up to 4,000 rounds per single run | Finite-round negotiation in a single session |
| Market environment | Dynamic economic engine (promotions, disasters, seasonal demand fluctuations) | Static or simplified negotiation scenarios |
| Operational complexity | Covers store opening, product selection, inventory, cash flow, returns, and fulfillment | Focuses only on price bargaining between buyers and sellers |
| Fraud detection evaluation | Built-in 152 fraudulent suppliers and 5 typical scam types | Does not involve fraud identification dimensions |
| Long-horizon learning measurement | AnchorRatio quantifies the learning effect of repeated procurement across time | Does not evaluate cross-session experience accumulation |
| Reproducible mechanism | Deterministic negotiation core + LLM renders dialogue only | Relies on LLM to play both negotiating parties simultaneously |
| Data source | Desensitized product and category data from real e-commerce platforms | Synthetic or manually constructed scenarios |
Application scenarios of E-Commerce Bench
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Large model business capability evaluation: Provides standardized horizontal comparison of long-horizon operational capabilities for frontier models such as GPT, Claude, and Qwen.
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E-commerce Agent strategy iteration: Serves as a training sandbox for reinforcement learning and Agent architectures to rapidly validate pricing, procurement, and inventory strategies.
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Supply chain risk control algorithm research: Tests and optimizes supplier screening and risk identification models in a simulated environment containing fraudulent suppliers.
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Dynamic pricing algorithm validation: Uses real price elasticity data to test the revenue performance of different pricing strategies under promotions and seasonal fluctuations.
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Business decision simulation training: Serves as a business simulator for business schools or enterprises to train managers' decision-making ability under cash flow pressure and market uncertainty.