guide
How to Use GLM-5.2 with EvoLink for Coding Agents
EvoLink Team
Product Team
June 18, 2026
4 min read
This guide shows how to use GLM-5.2 through EvoLink for coding agents, repo Q&A, long-context analysis, and tool-using assistants. The product page remains the source of truth for live pricing and route details: GLM-5.2 API on EvoLink.
The goal is a first successful call and a production handoff, not a full endpoint reference. For exact request parameters, see the GLM-5.2 API docs.
Treat the snippets below as OpenAI-compatible SDK patterns. Confirm exact parameter support in the GLM-5.2 API docs before production.
Quick Setup Path
| Step | What to do | Why it matters |
|---|---|---|
| 1 | Create an EvoLink API key | One key can route GLM-5.2 through the EvoLink gateway |
| 2 | Use an OpenAI-compatible client | Existing SDKs and coding-agent tools can usually be reused |
| 3 | Set model to glm-5.2 | Avoid slug/model-ID mismatches |
| 4 | Start with a small prompt | Confirm auth, routing, and response shape before adding tools |
| 5 | Add repo context and tools gradually | Control token cost and debug issues in smaller steps |
OpenAI-Compatible Python Pattern
from openai import OpenAI
client = OpenAI(
api_key="YOUR_EVOLINK_API_KEY",
base_url="https://api.evolink.ai/v1",
)
response = client.chat.completions.create(
model="glm-5.2",
messages=[
{"role": "system", "content": "You are a concise senior software engineer."},
{"role": "user", "content": "Review this function and suggest one safe refactor."},
],
temperature=0.2,
max_tokens=1024,
)
print(response.choices[0].message.content)OpenAI-Compatible Node.js Pattern
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.EVOLINK_API_KEY,
baseURL: "https://api.evolink.ai/v1",
});
const response = await client.chat.completions.create({
model: "glm-5.2",
messages: [
{ role: "system", content: "You are a concise senior software engineer." },
{ role: "user", content: "Summarize the risks in this pull request." },
],
temperature: 0.2,
max_tokens: 1024,
});
console.log(response.choices[0].message.content);When GLM-5.2 Fits a Coding-Agent Workflow
| Workflow | Good fit | Production note |
|---|---|---|
| Repo Q&A | Large context can reduce aggressive chunking | Cache stable repo prefixes when possible |
| Code review | Useful for multi-step reasoning over diffs | Keep output limits explicit |
| Tool-using agents | Function calling can support plan-act-observe loops | Add tool schemas after the basic call works |
| Long-document analysis | Works for contracts, specs, and reports | Track input tokens before sending full context |
| Coding CLIs | OpenAI-compatible route can simplify setup | See one gateway for coding CLIs |
Cost-Control Checklist
GLM-5.2 on EvoLink is priced by input, output, and cache-read tokens. Use the live table on the GLM-5.2 product page before estimating production cost.
- Keep stable system prompts and repository summaries at the beginning of the prompt.
- Reuse long prefixes when prompt caching applies.
- Disable deeper reasoning controls when a simple answer is enough.
- Put hard caps on
max_tokensfor agent loops. - Log input, output, cache-read, latency, and retry count per call.
Production Handoff
Before routing real coding-agent traffic, verify:
| Check | Pass condition |
|---|---|
| Auth | A fresh EvoLink key returns a successful response |
| Model ID | Requests use glm-5.2, not the page slug glm-5-2 |
| Cost | Input/output/cache-read usage is visible in billing or logs |
| Tool calls | Tool schemas work in a small test before full agent orchestration |
| Fallback | A second model or manual path exists for failed agent sessions |
FAQ
What model ID should I use?
Use
glm-5.2 in the request body. The product URL is /glm-5-2, but the request model ID uses a dot.Is GLM-5.2 compatible with the OpenAI SDK?
Yes. EvoLink exposes the route through an OpenAI-compatible
/v1/chat/completions path, so the standard OpenAI SDK can be used with the EvoLink base URL.Where should I check pricing?
Use the live pricing table on GLM-5.2 API on EvoLink. Do not hard-code blog estimates into production billing logic.
Can I use GLM-5.2 for coding agents?
Yes, it is a strong fit for repo Q&A, code review, long-context analysis, and tool-using agent workflows when you need one gateway path.
Should I start with tool calling immediately?
No. First verify auth, model ID, and a plain chat response. Then add tool schemas and agent orchestration in small steps.
Does prompt caching always reduce cost?
Only when your workload reuses stable prefixes that qualify for cache reads. Design prompts so system instructions and repeated repo context stay stable.
