How GLM-5‑Turbo’s “Lobster” Package Powers a Self‑Built Product Operations Assistant

GLM‑5‑Turbo is presented as the base model specially tuned for OpenClaw scenarios, where autonomous agents require multi‑turn understanding, task decomposition, tool invocation, temporal awareness, and long‑chain workflow execution—capabilities that generic chat‑oriented models struggle to deliver.

Key capability upgrades

Enhanced tool calling : more stable and reliable invocation of external tools and Skills across multi‑step tasks.

Complex instruction comprehension : stronger parsing of multi‑layer, long‑chain commands, enabling precise goal identification, step planning, and coordinated multi‑agent collaboration.

Temporal dimension awareness : optimized for timed triggers and continuous execution, maintaining continuity over extended tasks.

High throughput and long‑chain efficiency : improved response stability and execution speed for data‑heavy, logically deep OpenClaw workflows.

ZClawBench: an OpenClaw‑specific benchmark

The ZClawBench suite evaluates models on typical OpenClaw tasks such as installation, coding, information gathering, data analysis, and content creation. It uses scripted verification, an Agentic Judge, and pairwise comparisons to assess end‑to‑end agent performance. Results show GLM‑5‑Turbo markedly outperforms the generic GLM‑5 baseline and surpasses several mainstream models on critical tasks, demonstrating higher task‑completion ability and stability.

Community feedback and a practical demo

During internal testing, the ByteDance TRAE team praised GLM‑5‑Turbo’s programming ability, ranking it in the top tier for Coding Agent tasks and noting its robustness on long‑chain code assignments. The author, using Chrome 146’s DevTools MCP together with AutoClaw, built a “product‑operations assistant” that automatically pulls user data from Clerk, stores it in Feishu tables, and sends update‑notification emails.

Step‑by‑step, the workflow involved:

Installing Chrome MCP (version 146) and enabling remote debugging via chrome://inspect/#remote‑debugging.

Adding the 163 email‑sending Skill to AutoClaw.

Configuring the agent to read user records from Clerk, write them to a Feishu spreadsheet, and trigger email dispatch.

Testing the end‑to‑end process, confirming receipt of update emails, and finally scheduling the agent as a daily task.

The author emphasizes that the entire automation required no custom code—only a one‑time teaching interaction that the agent later retained as reusable Skills.

Token cost and the “Lobster” pricing plan

While GLM‑5‑Turbo delivers strong performance, it consumes a substantial number of tokens. To address cost concerns, Zhipu AI launched a “Lobster” package (personal and team tiers) offering the model at a heavily discounted rate compared with overseas alternatives. The package is limited to 10 000 purchases, with a special 3.4 × discount for AutoClaw users until March 22.