How to Use GLM-5 API with OpenClaw in 2026

How to Use GLM-5 API with OpenClaw in 2026

OpenClaw is a popular open-source automation framework that lets you build AI-powered workflows with modular, composable nodes. Pairing it with GLM-5 -- ZAI's powerful coding model with 128K context -- gives you a cost-effective pipeline for code generation, document analysis, and complex reasoning tasks.

This guide walks you through setting up GLM-5 in OpenClaw using Hypereal as your API provider.

Prerequisites

• An OpenClaw installation (v2.0+)
• A Hypereal API key -- sign up free at hypereal.ai (35 credits, no credit card required)
• Basic familiarity with OpenClaw workflows

Step 1: Get Your Hypereal API Key

1. Go to hypereal.ai and create an account
2. Navigate to the API Keys section in your dashboard
3. Generate a new API key and copy it

You get 35 free credits on signup, which is enough for extensive testing with GLM-5.

Step 2: Configure the API Provider in OpenClaw

OpenClaw supports custom LLM providers through its settings panel. Add Hypereal as a new provider:

{
  "providers": {
    "hypereal": {
      "type": "openai-compatible",
      "baseURL": "https://hypereal.tech/api/v1/chat",
      "apiKey": "your-hypereal-api-key",
      "models": ["glm-5"]
    }
  }
}

Alternatively, configure it through the OpenClaw UI:

1. Open Settings > LLM Providers
2. Click Add Provider
3. Select OpenAI-Compatible as the provider type
4. Enter the base URL: https://hypereal.tech/api/v1/chat
5. Paste your Hypereal API key
6. Add glm-5 to the model list

Step 3: Create a GLM-5 Workflow

Here is a simple OpenClaw workflow that uses GLM-5 to analyze code and suggest improvements:

name: code-review-pipeline
nodes:
  - id: input
    type: text-input
    config:
      label: "Paste your code"

  - id: analyze
    type: llm
    config:
      provider: hypereal
      model: glm-5
      system_prompt: |
        You are a senior code reviewer. Analyze the provided code for:
        1. Bugs and potential issues
        2. Performance improvements
        3. Code style and readability
        Provide specific, actionable feedback.
      temperature: 0.3
      max_tokens: 4096

  - id: output
    type: text-output
    config:
      label: "Review Results"

edges:
  - from: input
    to: analyze
  - from: analyze
    to: output

Step 4: Use GLM-5 in Code Nodes

For more control, use GLM-5 directly in OpenClaw code nodes:

import openai

def run(inputs):
    client = openai.OpenAI(
        api_key=inputs["api_key"],
        base_url="https://hypereal.tech/api/v1/chat"
    )

    response = client.chat.completions.create(
        model="glm-5",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": inputs["prompt"]}
        ],
        max_tokens=4096,
        temperature=0.3
    )

    return {"result": response.choices[0].message.content}

import OpenAI from "openai";

export async function run(inputs: { apiKey: string; prompt: string }) {
  const client = new OpenAI({
    apiKey: inputs.apiKey,
    baseURL: "https://hypereal.tech/api/v1/chat",
  });

  const response = await client.chat.completions.create({
    model: "glm-5",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: inputs.prompt },
    ],
    max_tokens: 4096,
    temperature: 0.3,
  });

  return { result: response.choices[0].message.content };
}

Advanced: Multi-Step Pipeline

GLM-5's 128K context window makes it ideal for multi-step workflows where context accumulates across nodes:

name: document-to-code
nodes:
  - id: doc-input
    type: file-input
    config:
      accept: [".md", ".txt", ".pdf"]

  - id: extract-requirements
    type: llm
    config:
      provider: hypereal
      model: glm-5
      system_prompt: "Extract all functional requirements from this document as a numbered list."
      temperature: 0.2
      max_tokens: 4096

  - id: generate-code
    type: llm
    config:
      provider: hypereal
      model: glm-5
      system_prompt: "Generate clean, production-ready Python code that implements the given requirements. Include type hints and docstrings."
      temperature: 0.3
      max_tokens: 8192

  - id: generate-tests
    type: llm
    config:
      provider: hypereal
      model: glm-5
      system_prompt: "Write comprehensive pytest tests for the provided code. Cover edge cases."
      temperature: 0.3
      max_tokens: 4096

  - id: output
    type: text-output

edges:
  - from: doc-input
    to: extract-requirements
  - from: extract-requirements
    to: generate-code
  - from: generate-code
    to: generate-tests
  - from: generate-tests
    to: output

Why GLM-5 + OpenClaw + Hypereal?

• Cost-effective -- GLM-5 through Hypereal is 40% cheaper than ZAI direct pricing ($0.60/$2.70 per 1M tokens in/out)
• 128K context -- handle large documents and multi-step workflows without truncation
• OpenAI-compatible -- no custom SDK needed, works with standard OpenAI client libraries
• Free to start -- 35 credits on Hypereal with no credit card required
• Strong coding -- GLM-5 excels at code generation, review, and refactoring tasks

Troubleshooting

• 401 Unauthorized -- double-check your API key in the provider settings
• Model not found -- ensure you are using glm-5 as the model name (case-sensitive)
• Timeout errors -- for long outputs, increase the timeout in your OpenClaw node configuration
• Rate limits -- Hypereal has generous rate limits, but if you hit them, add a delay node between LLM calls

Get Started

Try Hypereal AI free -- 35 credits, no credit card required.

Set up your API key, configure the provider in OpenClaw, and start building powerful AI workflows with GLM-5 in minutes.