GLM-5.2 OpenAI兼容API实践：推理努力、函数调用与长上下文
GLM-5.2 OpenAI-Compatible API: A Hands-On Guide to Reasoning Effort, Function Calling, and Long-Context Retrieval
这篇教程手把手教你用GLM-5.2 API实现推理控制、函数调用和检索，代码可直接复用。
本文通过GLM-5.2的OpenAI兼容API搭建了完整工作流，包括安全加载API密钥和创建可复用聊天封装。
演示了思考努力控制、流式推理、函数调用以及工具使用代理的实现。还展示了结构化JSON输出和长上下文检索功能，并记录了token消耗与成本核算。
GLM-5.2 OpenAI-Compatible API: A Hands-On Guide to Reasoning Effort, Function Calling, and Long-Context Retrieval
We build a practical GLM-5.2 workflow using its hosted, OpenAI-compatible API instead of running the model locally. We set up multiple providers, load the API key securely, and create a reusable chat wrapper. We then test thinking-effort control, streamed reasoning, function calling, a tool-using agent, structured JSON output, and long-context retrieval. We close with token and cost accounting so every demo stays measurable. The post GLM-5.2 OpenAI-Compatible API: A Hands-On Guide to Reasoning Effort, Function Calling, and Long-Context Retrieval appeared first on MarkTechPost .
This report is categorized under AITOP's "How-to" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include GLM-5.2, 推理模型, 函数调用, 长上下文.
Primary source verification: published directly by marktechpost with first-party authority. Cross-source corroboration: 9 independent sources have covered this development (including Decoder, Viking, IT之家), confirming industry-wide signal strength.
