GLM Agent Example

This example demonstrates how to use HNO with GLM (智谱AI), China's leading domestic LLM platform.

Overview

GLM (Zhipu AI) is an advanced language model developed by Tsinghua University's Knowledge Engineering Group. It offers:

• Optimized for Chinese: Excellent performance on Chinese language tasks
• GLM-4: Main conversational model with 128K context
• GLM-4V: Vision-enabled multimodal capabilities
• GLM-3-Turbo: Fast and cost-effective variant

Prerequisites

1. Go 1.21+ installed
2. GLM API Key from https://open.bigmodel.cn/

Getting Your API Key

1. Visit https://open.bigmodel.cn/
2. Sign up or log in
3. Navigate to API Keys section
4. Create a new API key

The API key format is: {key_id}.{key_secret}

Installation

go get github.com/rexleimo/agno-go

Environment Setup

Create a .env file or export the environment variable:

export ZHIPUAI_API_KEY=your-key-id.your-key-secret

Basic Example

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/rexleimo/agno-go/pkg/hno/agent"
    "github.com/rexleimo/agno-go/pkg/hno/models/glm"
)

func main() {
    // Create GLM model
    model, err := glm.New("glm-4", glm.Config{
        APIKey:      os.Getenv("ZHIPUAI_API_KEY"),
        Temperature: 0.7,
        MaxTokens:   1024,
    })
    if err != nil {
        log.Fatalf("Failed to create GLM model: %v", err)
    }

    // Create agent
    agent, err := agent.New(agent.Config{
        Name:         "GLM Assistant",
        Model:        model,
        Instructions: "You are a helpful AI assistant.",
    })
    if err != nil {
        log.Fatalf("Failed to create agent: %v", err)
    }

    // Run agent
    output, err := agent.Run(context.Background(), "Hello! Tell me about yourself.")
    if err != nil {
        log.Fatalf("Agent run failed: %v", err)
    }

    fmt.Println(output.Content)
}

Example with Tools

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/rexleimo/agno-go/pkg/hno/agent"
    "github.com/rexleimo/agno-go/pkg/hno/models/glm"
    "github.com/rexleimo/agno-go/pkg/hno/tools/calculator"
    "github.com/rexleimo/agno-go/pkg/hno/tools/toolkit"
)

func main() {
    // Create GLM model
    model, err := glm.New("glm-4", glm.Config{
        APIKey:      os.Getenv("ZHIPUAI_API_KEY"),
        Temperature: 0.7,
        MaxTokens:   1024,
    })
    if err != nil {
        log.Fatal(err)
    }

    // Create agent with calculator tools
    agent, err := agent.New(agent.Config{
        Name:         "GLM Calculator Agent",
        Model:        model,
        Toolkits:     []toolkit.Toolkit{calculator.New()},
        Instructions: "You are a helpful AI assistant that can perform calculations.",
    })
    if err != nil {
        log.Fatal(err)
    }

    // Test calculation
    output, err := agent.Run(context.Background(), "What is 123 * 456?")
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("Result: %s\n", output.Content)
}

Chinese Language Example

GLM excels at Chinese language tasks:

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/rexleimo/agno-go/pkg/hno/agent"
    "github.com/rexleimo/agno-go/pkg/hno/models/glm"
)

func main() {
    model, err := glm.New("glm-4", glm.Config{
        APIKey:      os.Getenv("ZHIPUAI_API_KEY"),
        Temperature: 0.7,
    })
    if err != nil {
        log.Fatal(err)
    }

    agent, err := agent.New(agent.Config{
        Name:         "中文助手",
        Model:        model,
        Instructions: "你是一个有用的中文AI助手。",
    })
    if err != nil {
        log.Fatal(err)
    }

    // Ask in Chinese
    output, err := agent.Run(context.Background(), "请用中文介绍一下人工智能的发展历史。")
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(output.Content)
}

Running the Example

1. Clone the repository:

git clone https://github.com/rexleimo/agno-go.git
cd HNO

2. Set your API key:

export ZHIPUAI_API_KEY=your-key-id.your-key-secret

3. Run the example:

go run cmd/examples/glm_agent/main.go

Configuration Options

glm.Config{
    APIKey:      string  // Required: {key_id}.{key_secret} format
    BaseURL:     string  // Optional: Custom API endpoint
    Temperature: float64 // Optional: 0.0-1.0 (default: 0.7)
    MaxTokens:   int     // Optional: Max response tokens
    TopP:        float64 // Optional: Top-p sampling parameter
    DoSample:    bool    // Optional: Enable sampling
}

Authentication

GLM uses JWT (JSON Web Token) authentication:

• API key is split into key_id and key_secret
• JWT token is generated using HMAC-SHA256 signing
• Token is valid for 7 days
• Automatically handled by the SDK

Supported Models

Model	Context	Best For
glm-4	128K	General conversation, Chinese language
glm-4v	128K	Vision tasks, multimodal
glm-3-turbo	128K	Fast responses, cost-effective

Common Issues

Invalid API Key Format

Problem: API key must be in format {key_id}.{key_secret}

Solution: Ensure your API key contains a dot (.) separator between key_id and key_secret.

Authentication Failed

Problem: GLM API error: Invalid API key

Solution:

• Verify your API key is correct
• Check if the API key is active at https://open.bigmodel.cn/
• Ensure no extra spaces in the environment variable

Rate Limiting

Problem: GLM API error: Rate limit exceeded

Solution:

• Implement retry logic with exponential backoff
• Reduce request frequency
• Upgrade your API plan if needed

Next Steps

• Learn about Models for more LLM options
• Add more Tools to enhance capabilities
• Build Teams with multiple agents
• Explore Workflows for complex processes

Related Examples

• Simple Agent - OpenAI example
• Claude Agent - Anthropic example
• Team Demo - Multi-agent collaboration

Resources

• GLM Official Website
• GLM API Documentation
• HNO Repository