OpenClaw & Hermes Agent Domestic Model Integration: GLM, Kimi, DeepSeek One-Click Setup Guide

Domestic Models + Agent Frameworks: A Key Step in Ecosystem Integration

In the first half of 2026, AI Agent frameworks have reached a milestone in model compatibility: OpenClaw and Hermes Agent now fully support major domestic Chinese AI models.

This means developers are no longer locked into a single model vendor’s ecosystem. You can switch between GLM-5.1, Kimi K2.6, DeepSeek V4 Pro, Qwen 3.6 and other models within the same Agent framework, selecting based on task type and cost budget.

Community products already offer “zero-config, one-click integration” experiences where you just fill in your AI Key and everything works. This marks a transition from “can connect” to “works well” for domestic models in Agent framework ecosystems.

Supported Domestic Models Overview

Model	Provider	Integration Method	Best Use Case
GLM-5.1	Zhipu AI	API / OpenClaw built-in / Hermes MCP	Coding Agent, code review
Kimi K2.6	Moonshot	API / Hermes built-in / OpenClaw	Long context, large codebases
DeepSeek V4 Pro	DeepSeek	API / OpenClaw built-in	Cost-effective coding, debug analysis
Qwen 3.6 Max	Alibaba	API / Hermes MCP	Agent coding, multi-file collaboration
MiniMax M2.7	MiniMax	API	High-frequency Agent calls
MIMO V2.5 Pro	Xiaomi	API / Ollama	Code Agent, edge deployment

OpenClaw Integration Configuration

OpenClaw model configuration is managed through .openclaw/config.yaml, supporting multi-model switching.

GLM-5.1 Integration

models:
  glm-5.1:
    provider: zhipu
    apiKey: "${ZHIPU_API_KEY}"
    model: glm-5.1
    contextWindow: 131072
    maxTokens: 8192
    temperature: 0.3

GLM-5.1’s advantage in OpenClaw is code generation stability. In testing, GLM-5.1 maintains variable naming conventions across 20+ rounds of continuous coding dialogue—the strongest among domestic models.

Kimi K2.6 Integration

models:
  kimi-k26:
    provider: moonshot
    apiKey: "${MOONSHOT_API_KEY}"
    model: kimi-k2.6
    contextWindow: 256000
    maxTokens: 16384
    temperature: 0.2

Kimi K2.6’s 256K context window is its core advantage. In large codebase scenarios—like refactoring tasks requiring simultaneous reading of dozens of source files—Kimi significantly outperforms other domestic models.

DeepSeek V4 Pro Integration

models:
  deepseek-v4-pro:
    provider: deepseek
    apiKey: "${DEEPSEEK_API_KEY}"
    model: deepseek-v4-pro
    contextWindow: 128000
    maxTokens: 8192
    temperature: 0.4

DeepSeek V4 Pro’s core competitiveness is cost efficiency. Its per-token cost is the lowest among domestic models, ideal for Agent workflows requiring high-volume API calls.

Hermes Agent Integration Configuration

Hermes Agent supports domestic models through both MCP (Model Context Protocol) and built-in model configuration.

MCP Integration

{
  "mcpServers": {
    "zhipu-glm": {
      "command": "npx",
      "args": ["-y", "@zhipu/mcp-server"],
      "env": {
        "ZHIPU_API_KEY": "${ZHIPU_API_KEY}",
        "MODEL": "glm-5.1"
      }
    },
    "moonshot-kimi": {
      "command": "npx",
      "args": ["-y", "@moonshot/mcp-server"],
      "env": {
        "MOONSHOT_API_KEY": "${MOONSHOT_API_KEY}",
        "MODEL": "kimi-k2.6"
      }
    }
  }
}

Built-in Model Configuration

{
  "defaultModel": "kimi-k2.6",
  "models": {
    "kimi-k2.6": {
      "provider": "moonshot",
      "apiKey": "${MOONSHOT_API_KEY}",
      "capabilities": ["code", "long-context", "reasoning"]
    },
    "glm-5.1": {
      "provider": "zhipu",
      "apiKey": "${ZHIPU_API_KEY}",
      "capabilities": ["code", "tool-use"]
    },
    "deepseek-v4-pro": {
      "provider": "deepseek",
      "apiKey": "${DEEPSEEK_API_KEY}",
      "capabilities": ["code", "debug", "reasoning"]
    }
  }
}

Cost Comparison: Domestic Models in Agent Scenarios

For Agent workflows requiring high-frequency calls, model selection directly impacts operational costs. Below assumes 100 Agent calls daily, averaging 8000 input + 4000 output tokens per call:

Model	Input ($/M)	Output ($/M)	Daily Cost	Monthly Cost
GLM-5.1 Coding Plan	Subscription	Subscription	—	¥469
Kimi K2.6	~$0.50	~$1.00	~$0.07	~$2.10
DeepSeek V4 Pro	$0.60	$1.20	$0.072	$2.16
Qwen 3.6 Plus	~$0.30	~$0.60	$0.036	$1.08
MiniMax M2.7	$0.30	TBD	< $0.05	< $1.50

Key finding: Domestic model Agent usage costs are only 1/10 to 1/20 of GPT-5.5, providing economically viable Agent deployment for individual developers and small teams.

Practical Tips

1. Model Routing: Auto-Switch by Task Type

routing:
  rules:
    - pattern: ".*code generation|write function|implement.*"
      model: glm-5.1
      reason: "Best code generation stability"
    - pattern: ".*analyze.*files|refactor.*project|.*all code.*"
      model: kimi-k2.6
      reason: "Long context advantage"
    - pattern: ".*debug|find.*bug|.*why error.*"
      model: deepseek-v4-pro
      reason: "Complete reasoning chain"
    - pattern: ".*translate|summarize.*"
      model: qwen-3.6-plus
      reason: "Best cost-performance"

2. Fallback Strategy

fallback:
  primary: glm-5.1
  secondary: kimi-k2.6
  tertiary: deepseek-v4-pro
  maxRetries: 3

3. Context Management: Optimizing Domestic Model Context

While domestic models support large context windows, their effective attention range is limited:

GLM-5.1: Effective context ~64K, recommend chunking for large codebases
Kimi K2.6: Effective context ~128K, best among domestic models
DeepSeek V4 Pro: Effective context ~64K, suitable for medium-scale tasks

Model Selection Decision Tree

What is your primary task?
├── Code generation / feature implementation
│   ├── Need stable high-quality output? → GLM-5.1
│   └── Budget-sensitive? → Qwen 3.6 Plus
├── Large codebase / multi-file refactoring
│   └── → Kimi K2.6 (long context)
├── Debug / troubleshooting
│   └── → DeepSeek V4 Pro (complete reasoning)
├── High-frequency Agent loops
│   └── → MiniMax M2.7 (lowest cost)
└── General tasks / daily assistance
    └── → Qwen 3.6 Plus (best value)

Summary

In H1 2026, the integration of domestic models in Agent frameworks has undergone a qualitative leap. From “API-only connection” to “zero-config one-click integration”, from “barely usable” to “reliably effective”, domestic models are playing an increasingly important role in the Agent ecosystem.

For developers, this means a key shift: you no longer need to rely on a single model vendor. Like choosing a database, you can flexibly combine domestic models based on task type, performance needs, and budget to build optimal Agent workflows.

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