How Domestic Large Models Take Over OpenClaw: Kimi, MiniMax, and Qwen's Agent Ecosystem

For dedicated OpenClaw users, relying solely on Claude 3.5 Sonnet or GPT-4o for automation is becoming increasingly difficult. As Anthropic tightens restrictions on third-party OAuth tokens, "ban anxiety" looms over developers, while high Token consumption drives up the cost of 24/7 Agents. This forces a re-evaluation of infrastructure, shifting focus to domestic models like Kimi, MiniMax, and Qwen. This is not merely an API replacement, but a complete overhaul of cost-efficiency and service continuity. With proper OpenClaw Kimi MiniMax Config, developers can slash monthly bills from $200 to under$15, while utilizing the OpenClaw fallback strategy to mitigate risks associated with single-provider instability. This article details how to modify the OpenClaw config json file to seamlessly integrate these cheap OpenClaw models into existing Agent ecosystems, covering everything from API key acquisition to OpenClaw token limits optimization. Combining recent Qwen agent benchmarks and stress tests, we verify the performance of these models in long-context reasoning and complex instruction following. Mastering this OpenClaw ban avoidance and cost-efficiency solution is essential for building a stable, enterprise-grade, and highly cost-effective autonomous Agent system, ending blind reliance on expensive overseas computing power.

Why Choose Domestic Models: The Dual Game of Cost and Stability

For heavy users of OpenClaw, relying on Claude 3.5 Sonnet or GPT-4o for 24/7 Agent hosting is facing unprecedented challenges. This is not merely a cost issue, but a survival game regarding service continuity. As vendors like Anthropic tighten compliance for third-party automation tools, migrating infrastructure to domestic large models (Kimi, MiniMax, Qwen) is no longer just a simple "budget alternative" choice, but a necessary means to build a risk-resistant Agent ecosystem.

Saying Goodbye to "Ban Anxiety": Compliance and API Stability

In the past, many developers were accustomed to accessing OpenClaw via OAuth tokens from Claude Pro/Max accounts to leverage their powerful reasoning capabilities. However, the risks of this practice have fully erupted. According to community feedback and actual testing, Anthropic has explicitly prohibited the use of personal subscription OAuth tokens in third-party tools, with violators facing severe penalties of account bans without any grace period.

In contrast, domestic large models provide standardized API services through official open platforms and hold a supportive attitude towards the Agent ecosystem. Using enterprise-grade APIs from Kimi (Moonshot AI) or MiniMax not only avoids the ban risk associated with "gray market" access but also secures higher concurrency quotas (QPS), ensuring that Agents do not crash due to rate limits when performing multi-step reasoning tasks.

The Cost Equation: A Drastic Drop from $200 to$15

OpenClaw's operating mechanism dictates that it is a token-devouring beast. When an autonomous Agent executes a task, it often needs to go through a "Think-Plan-Tool Use-Reflect-Correct" loop. This Chain of Thought (CoT) process generates massive amounts of intermediate tokens, causing API bills to grow exponentially.

The following is a cost comparison of mainstream models in the OpenClaw scenario (based on price per million tokens):

Note: Prices are estimated based on official and LLM Stats comparison data; actual exchange rate fluctuations may cause minor adjustments.

As can be seen from the table above, the comprehensive cost of Kimi K2.5 is only about 1/7 of Claude 3.5 Sonnet. This means that with the same budget, your Agent can attempt wrong paths and self-correct 7 more times, which is crucial for the success rate of automated tasks.

Real-World Scenario: A $15/Month Fully Functional Agent Solution

To concretize this cost difference, we refer to Rentier Digital's actual test case to build a typical 24/7 running scenario:

• Old Scheme (Claude Dependent):
• • Model: Claude 3.5 Sonnet via API
  • Load: Processing emails, schedules, and simple social media monitoring daily.
  • Monthly Cost: Due to the Agent's redundant thinking and retry mechanisms, API fees easily exceed $200/month.
  • Risk: High-frequency calls trigger risk control, leading to API Key invalidation.
• New Scheme (Domestic Ecosystem):
• • Main Model: Kimi K2.5 (Responsible for primary reasoning and context understanding, Context Window up to 262K).
  • Backup Model: MiniMax or Qwen (As a Fallback link, handling simple tasks or taking over when the main interface fluctuates).
  • Infrastructure: Two low-cost VPSs for redundant deployment.
  • Monthly Cost: VPS (~$10) + API consumption (~$5) = ~$15/month.

This architecture not only compresses costs by over 90% but also introduces enterprise-grade redundancy design. When Kimi experiences occasional fluctuations, OpenClaw can be configured to automatically switch to MiniMax, ensuring business processes are not interrupted. For the vast majority of non-research Agent tasks (such as information organization, copy generation, instant messaging replies), the reasoning capabilities of domestic models are fully competent, and in terms of long text (Long Context) processing, Kimi K2.5's performance even outperforms the old version of GPT-4 in certain benchmarks.

Core Configuration in Action: From API Acquisition to JSON Deployment

After completing the assessment of cost and stability, the next key step is to integrate domestic large models into OpenClaw's runtime environment. This involves not only simple API Key replacement but also requires precise adjustments to OpenClaw's core configuration file to ensure the Agent's response speed and fault tolerance when handling complex tasks.

OpenClaw's configuration is typically located in the ~/.openclaw/openclaw.json file in the user's home directory (some Docker deployments may locate it at /root/.openclaw/). This JSON file controls the Agent's model routing, context window size, and concurrency limits. Before making any modifications, it is recommended to back up the original configuration file, as minor errors in JSON syntax (such as an extra comma or a missing bracket) can directly cause the Agent to fail to start or crash during runtime.

To build a high-availability Agent system, we do not recommend relying solely on a single model. OpenClaw's architecture allows defining primary and fallbacks queues. In actual deployment, the best practice is to set models with strong reasoning capabilities, such as Kimi or Qwen, as the primary force, while configuring MiniMax or other high-throughput models as fallbacks to cope with occasional API rate limiting or service fluctuations. The following section will detail how to populate the configuration with these domestic models one by one to build an automated workflow that is both economical and robust.

Configure Kimi (Moonshot AI) as the Primary Agent

Kimi (Moonshot AI) is currently the preferred choice for replacing Claude in Chinese context reasoning. Its API is fully compatible with the OpenAI format, which significantly reduces the difficulty of integration in OpenClaw. Compared to forwarding through a middleware layer, directly calling the official Moonshot API offers lower latency and higher stability.

1. Obtain API Key

First, log in to the Moonshot open platform console to create an API Key. It is recommended to create a separate Key for OpenClaw (e.g., named OpenClaw-Agent) to facilitate independent tracking of Token consumption or to revoke it individually in case of a leak.

2. Modify Configuration File

Locate OpenClaw's core configuration file (usually located at ~/.openclaw/config.json or config.json in the project root directory). You need to define Moonshot's connection parameters within a custom provider block and set it as the primary model.

Below is a battle-tested configuration code block. Please note that base_url must be filled in accurately, and it is recommended to use moonshot-v1-128k as the model ID to handle the Agent's complex context history:

{
  "models": {
    "providers": {
      "moonshot": {
        "type": "openai",
        "baseUrl": "https://api.moonshot.cn/v1",
        "apiKey": "sk-YOURMOONSHOTAPIKEYHERE",
        "models": [
          {
            "id": "moonshot-v1-128k",
            "name": "Kimi 128k",
            "cost": {
              "input": 0.012,
              "output": 0.012
            }
          },
          {
            "id": "moonshot-v1-8k",
            "name": "Kimi 8k"
          }
        ]
      }
    },
    "model": {
      "primary": "moonshot/moonshot-v1-128k",
      "fallbacks": ["minimax/MiniMax-M2.5"]
    }
  }
}

Key Parameter Analysis

• base_url: Moonshot's official endpoint is https://api.moonshot.cn/v1. OpenClaw relies on this path for the OpenAI protocol handshake; an incorrect path will result in a 404 Not Found or protocol parsing failure.
• model.primary: Specify as moonshot/moonshot-v1-128k. Although v1-8k is cheaper, during Agent operation, the stack of multi-turn conversations and tool calls can easily exceed the 8k limit, causing the Agent to fall into an infinite loop or lose its task objective.
• fallbacks: It is recommended to configure MiniMax or Qwen as alternatives. When Kimi encounters concurrency limits (Rate Limit), OpenClaw will automatically switch to the backup model to ensure continuous operation.

Context Window and Performance Warnings

Although Kimi K2.5 performs excellently in benchmarks, note the following differences when migrating existing Claude workflows:

1. "Needle in a Haystack" Capability: Claude 3.5 Sonnet maintains extremely high instruction adherence within a 200k context, whereas Kimi may experience format drift regarding complex nested JSON output instructions when approaching the 128k limit. It is recommended to add a "Force JSON format check" system prompt.
2. Concurrency Limits: The default RPM (Requests Per Minute) for individual Moonshot developers is relatively low. If your OpenClaw runs multi-threaded tasks (such as parallel crawlers), it is very easy to trigger 429 Too Many Requests. For high-frequency tasks, be sure to enable the fallbacks mechanism in the configuration or apply to the official team for a quota increase.

Integrating MiniMax and Qwen: Bridging the Reasoning Gap

Although Kimi excels in long-text processing, introducing MiniMax and Qwen can significantly compensate for the shortcomings of a single model in specific Agent tasks. OpenClaw's multi-model architecture allows developers to dynamically switch underlying models based on task type (such as code generation or social interaction), thereby optimizing Token costs and output quality.

MiniMax: The Expert in Social Interaction and Role-Playing
MiniMax (especially the abab6.5 series) stands out in human-like conversation and "high EQ" responses, making it very suitable for OpenClaw's Outreach module. Unlike the standard OpenAI format, MiniMax's native API often requires configuring a group_id, which is a common reason for integration failures among many developers.

In OpenClaw's providers.json, the standard configuration for integrating MiniMax is as follows:

{
  "provider": "minimax",
  "model": "abab6.5s-chat",
  "apikey": "YOURMINIMAXAPIKEY",
  "baseurl": "https://api.minimax.chat/v1",
  "parameters": {
    "temperature": 0.7,
    "groupid": "YOURGROUPID", 
    "botsetting": [
      {
        "botname": "OpenClaw_Agent",
        "content": "You are a professional business development expert with a sincere and professional tone."
      }
    ]
  }
}

Note: group_id is a required field for the MiniMax V1 API; leaving it blank will result in a 400 error. If using relay services like OneAPI, you may need to map it to an OpenAI-compatible format, but this parameter must be retained when integrating natively.

Qwen (Tongyi Qianwen): The Engine for Code Logic and Complex Reasoning
For tasks involving JSON formatting, Python script generation, or complex logical reasoning, Qwen-Max or Qwen-2.5-72B are currently the best choices among Chinese models. Their performance in code benchmarks like HumanEval and MBPP often exceeds Kimi, effectively reducing syntax errors when the Agent executes Tool Use.

The configuration for integrating Qwen usually follows the standard OpenAI-compatible protocol, but attention must be paid to the accuracy of the model name:

{
  "provider": "aliyun",
  "model": "qwen-max",
  "apikey": "YOURDASHSCOPEAPIKEY",
  "baseurl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
  "parameters": {
    "enablesearch": false,
    "result_format": "message"
  }
}

Selection Advice: It is recommended to set rules in OpenClaw's task_router—route all tasks with type: "coding" or type: "data_analysis" to Qwen, and route tasks with type: "communication" to MiniMax.

Building a High-Availability Anti-Ban Solution: Fallback Strategy Configuration

In long-running automation tasks, relying solely on a single API makes it very easy to crash the entire Agent workflow due to triggering risk controls (429 Rate Limit) or service fluctuations (5xx Server Error). OpenClaw allows solving this pain point by configuring a Fallback Chain.

Fallback Logic Mechanism
When the Primary Model request fails or the response times out beyond the set threshold (e.g., 30 seconds), the system automatically captures the exception and seamlessly switches to a backup model for a retry. This is not only technical redundancy but also a strategic defense against API banning risks.

The following is an example of a high-availability "anti-ban" configuration. This strategy uses Kimi, with the strongest long-text capability, as the primary force, MiniMax as a cost-effective alternative, and finally Qwen, with extremely strong instruction-following capability, as the backstop:

{
  "agentconfig": {
    "retrystrategy": {
      "maxretries": 3,
      "retrydelay": 2
    },
    "modelchain": [
      {
        "role": "primary",
        "provider": "moonshot",
        "model": "moonshot-v1-128k",
        "timeout": 60
      },
      {
        "role": "fallback1",
        "provider": "minimax",
        "model": "abab6.5s-chat",
        "condition": ["error429", "error5xx", "timeout"]
      },
      {
        "role": "fallback2",
        "provider": "aliyun",
        "model": "qwen-max",
        "condition": ["error4xx", "content_filter"]
      }
    ]
  }
}

Configuration Analysis:

1. Primary (Kimi): Handles the vast majority of daily requests, utilizing its 128k long context advantage to process complex historical conversation records.
2. Fallback 1 (MiniMax): Immediately switches to MiniMax once Kimi returns a 429 (Too Many Requests) or times out. MiniMax usually has looser concurrency limits and lower costs, making it suitable as the first line of defense.
3. Fallback 2 (Qwen): Acts as the final "goalkeeper". If the first two fail, or if content risk control issues (Content Filter) are encountered, Qwen's extremely strong instruction-following capability can usually ensure the task at least completes the loop, preventing the Agent from hanging.

Through this "three-stage rocket" style configuration, developers can ensure that OpenClaw maintains 99.9% operational availability in unattended scenarios, completely saying goodbye to task interruptions caused by fluctuations in a single API.

Building a High-Availability Anti-Blocking Solution: Fallback Strategy Configuration

In the long-chain tasks of automated Agents (such as LinkedIn automated prospecting or complex code refactoring loops), the stability of a single model is often the biggest single point of failure. Whether it is sporadic 5xx server errors from the API or sudden 429 Rate Limits, once the primary model is interrupted, the entire Agent process crashes, causing all previously completed work to be wasted.

To achieve production-grade High Availability, configuring Model Chaining in OpenClaw is mandatory. By defining a Fallback strategy, we can ensure that when the primary model (Kimi) is unavailable, the system can seamlessly switch to backup models (MiniMax or Qwen), thereby "resurrecting" stalled tasks.

Core Configuration Logic

The following is a standard "three-stage rocket" configuration scheme. This strategy sets Kimi, which has the strongest reasoning capability and best context support, as Primary; sets MiniMax, which has fast response speeds and looser concurrency limits, as the first alternative; and finally uses the highly stable Qwen as the safety net.

Recommended Fallback Chain:

1. Primary: Moonshot Kimi (K2.5/V1) — Responsible for core logic and complex instruction following.
2. Fallback 1: MiniMax (abab6.5) — Takes over when Kimi triggers RPM/TPM limits, maintaining high-speed output.
3. Fallback 2: Qwen (Qwen-Max) — The final line of defense, ensuring task closure.

Quick Config Code Block

In OpenClaw's config.json or model configuration file, you need to explicitly define the fallback_models array. The following configuration has been optimized for the domestic API environment:

{
  "llmstrategy": {
    "retryonfailure": true,
    "maxretries": 3,
    "fallbacktriggercodes": [429, 500, 502, 503],
    "modelchain": [
      {
        "role": "primary",
        "provider": "moonshot",
        "modelname": "moonshot-v1-128k",
        "apikeyenv": "MOONSHOTAPIKEY",
        "parameters": {
          "temperature": 0.3
        }
      },
      {
        "role": "fallback1",
        "provider": "minimax",
        "modelname": "abab6.5-chat",
        "apikeyenv": "MINIMAXAPIKEY",
        "note": "Switches here on Kimi 429/5xx errors. High throughput."
      },
      {
        "role": "fallback2",
        "provider": "aliyun",
        "modelname": "qwen-max",
        "apikeyenv": "DASHSCOPEAPIKEY",
        "note": "Final resort to prevent task crash."
      }
    ]
  }
}

Strategy Mechanism and Precautions

Once the configuration takes effect, OpenClaw's gateway layer will monitor HTTP response status codes.

1. Error Capture: When the primary model returns 429 Too Many Requests (common with Kimi's free or low-tier paid levels during high-frequency calls) or 502 Bad Gateway, the system will not throw an exception to terminate the program.
2. Automatic Switch: The Agent will automatically capture the context of the request (Prompt + History) and repackage it to send to fallback_1 (MiniMax). The user side is almost unaware, only seeing a [WARN] Primary model failed, failing over to MiniMax prompt in the logs.
3. Context Consistency Risk: Although Fallback ensures task survival, one must note the differences in Prompt sensitivity among different models. Kimi has strong instruction-following capabilities for long Contexts, whereas after switching to MiniMax, extremely complex JSON formatting requirements may need more explicit System Prompt constraints. It is recommended to add defensive instructions (Defensive Prompting) in the System Prompt, such as explicitly requesting "Output JSON only, do not include Markdown code block markers," to accommodate all alternative models.

Through this "primary-backup failover" mechanism, the risk of instability from a single provider can be effectively avoided, increasing the Agent's long-running success rate from 80% to over 99%. For irreversible operations involving fund consumption or external API interactions, this high-availability configuration is a baseline requirement for engineering implementation.

Performance Benchmark: Performance of Chinese Models in Agent Tasks

In the actual deployment of OpenClaw, cost advantage is only one side of the coin. For automated Agents, Stability and Instruction Following are the keys to determining whether they can run unattended.

Although Moonshot's Kimi K2 Thinking claims to surpass Claude Sonnet 4.5 in some benchmarks, in actual Agent Orchestration scenarios, there are still obvious "skill boundaries" between Chinese models and industry benchmarks like Claude 3.5 Sonnet/Opus.

The following is an empirical evaluation based on real OpenClaw workflows, aiming to help developers find a balance between "cost reduction" and "high availability."

Core Capability Comparison: JSON Formatting and Logical Reasoning

Agent frameworks like OpenClaw heavily rely on models outputting strict JSON formats to invoke Tools. If a model outputs an extra sentence outside the JSON, such as "Okay, here is the data you need," the entire automation chain will be interrupted due to parsing errors.

1. JSON Formatting Consistency

• • Claude 3.5 Sonnet: Performs extremely stably, requiring almost no extra Prompt constraints to output clean JSON.
  • Qwen-Max / Qwen-2.5: Performs excellently in instruction following, strictly executing the "output JSON only" instruction in the System Prompt, making it the top choice among Chinese models to replace Claude for tool invocation.
  • Kimi (Moonshot): Performs well in long text generation, but occasionally makes formatting errors (such as missing closing brackets) in complex nested JSON generation, or includes "thinking process" text before or after the JSON. It is recommended to forcibly add Few-Shot examples in the Prompt to improve stability.

1. Multi-step Reasoning & Context

• • MiniMax (abab6.5): Possesses 100% retrieval capability within a 200K context, making it outstanding in handling RAG (Retrieval-Augmented Generation) tasks. For example, asking an Agent to read a 50-page PDF and extract specific contact information, MiniMax has extremely high accuracy and extremely low cost.
  • Shortcoming: When involving complex chain tasks with more than 5 steps (e.g., Search -> Filter -> Visit Homepage -> Summarize -> Write Personalized Email), Chinese models are prone to "Goal Drift," meaning they forget the tone constraints set in step 1 by step 4.

Typical Task Pass/Fail Evaluation

To intuitively demonstrate the applicable scenarios for each model, we conducted "Pass/Fail" tests on common OpenClaw automation tasks. The test standard is not "whether it can complete it," but "whether it can run successfully 50 times continuously without human intervention."

Conclusion: 90% of Tasks Can Be Taken Over by Chinese Models

Empirical data shows that Chinese large models are not perfect 1:1 replacements, but excellent "layered" alternative solutions.

• Execution Layer (Worker): For tasks that consume the most Tokens (accounting for 80%-90% of the total), such as text generation, translation, and simple extraction, MiniMax or Kimi can fully take over, reducing costs by over 90%.
• Decision Layer (Brain): For core nodes that determine the Agent's next move and handle complex error logic, it is recommended to retain Claude 3.5 Sonnet or GPT-4o.

Through this "Hybrid Model Architecture," developers can enjoy the low Token prices of Chinese models while ensuring the robustness of core business logic.

Risk Control and Cost Management: How to Avoid "Bill Explosion" and "Account Bans"

In the process of migrating from Claude or GPT-4 to domestic large models (such as Kimi, MiniMax, or Qwen), developers often focus most on API connectivity but tend to overlook two fatal hidden dangers: "bill explosion" caused by uncontrolled Token consumption, and permanent account bans caused by misunderstanding risk control mechanisms. For automated Agents, the premise of stable operation is strict boundary control.

Clarifying Two Types of "Ban" Mechanisms: API Ban vs. Platform Ban

When many users in the OpenClaw community report "being banned," they often confuse the source of the ban. To establish an effective risk control strategy, one must first distinguish between two distinctly different risks:

1. API Provider Ban (Provider Side Ban):
   This refers to scenarios where LLM providers (such as Anthropic or OpenAI) detect that your API Key is being used in violation of the Terms of Service (ToS), such as high-frequency automated interactions or unauthorized third-party wrappers.

• • Current Status: As described in the Anthropic banning third-party tools case, many users using Claude with OpenClaw encountered OAuth Token invalidation or account bans because the official policy prohibits large-scale automated operations via API in unofficial clients.
  • Advantages of Domestic Models: The main advantage of switching to Kimi or MiniMax is that currently, domestic model vendors have a relatively higher tolerance for API concurrency and automation scenarios. Furthermore, when calling through aggregated API platforms (such as relay providers like APIYI), the risk to the main account can be further isolated.

1. Target Platform Ban (Platform Side Ban):
   This refers to the target website operated by your Agent (such as LinkedIn, Twitter, Xiaohongshu) detecting bot behavior.

• • Key Misconception: Changing models (switching from Claude to Kimi) does not reduce this risk. The risk control algorithms of target platforms detect browser fingerprints, click frequency, IP purity, and non-human characteristics of operations (such as 0-latency clicks). Whether the brain driving it is Kimi or GPT-4, if the Agent accesses 50 pages within 1 second, the target platform will immediately trigger risk control.

Avoiding "Bill Explosion": Defense Against Infinite Loops and Token Abuse

The core logic of Agent frameworks like OpenClaw is the "Observe-Think-Act" loop. If unrestricted, this loop can easily evolve into a "money-burning machine".

1. Beware of the "Infinite Loop" Trap

In actual deployment, the most common accident is the Agent falling into a task infinite loop. For example, the Agent attempts to click a non-existent button, fails and reports an error, the LLM receives the error and tries to retry, and fails again. This process may generate dozens of API calls per minute. Since OpenClaw carries the full historical context, the Token consumption for every retry will grow exponentially with the accumulation of conversation history. Without intervention, a simple UI automation task could exhaust hundreds of dollars in quota overnight.

2. Configure Hard Circuit Breaker Mechanisms

To prevent the above situation, strict limits must be set in config.json or environment variables. Do not rely on the model's "self-awareness"; physical blocking must be implemented at the code level.

The suggested configuration strategy is as follows:

• Set Maximum Iterations (Recursion Limit): Limit the maximum number of thinking rounds for the Agent in a single task.
• Configure Token Limit (Max Tokens): For domestic models, although the unit price is cheap, abnormal traffic must still be prevented.

{
  "llm": {
    "model": "moonshot-v1-8k",
    "temperature": 0.1,
    "maxtokens": 2048,  // Limit single response length, prevent model from outputting redundant nonsense
    "requesttimeout": 60
  },
  "agent": {
    "maxloops": 10,     // Key setting: Force stop after 10 rounds of interaction to prevent infinite loops
    "budgetlimit": 5.0  // If using a relay layer supporting budget control, set daily amount cap
  }
}

3. Optimize Context Window (Context Management)

Domestic large models (such as Kimi) usually support ultra-long contexts (200k+), which improves capability but also increases cost risks. During operation, the Agent will stuff webpage HTML source code and historical error messages into the Prompt. It is recommended to explicitly require the model to "only focus on key information" in the Prompt, or enable the history truncation function in OpenClaw's configuration to avoid repeatedly sending irrelevant webpage DOM structures to the API.

Security Recommendations

• Sandbox Execution: Given the potential Prompt injection risks of Agents and accidental operations on local files (such as accidentally deleting emails or files), it is recommended to always run OpenClaw in a Docker container or a non-production environment virtual machine, and strictly limit its file system read/write permissions.
• Human-in-the-loop: For steps involving funds (such as automatic ordering) or sensitive operations (such as sending messages, deleting data), auto_approve: false should be configured to strictly require manual confirmation by entering y in the terminal before execution.

Troubleshooting

When migrating OpenClaw's underlying model from Claude to Kimi, MiniMax, or Qwen, developers often encounter specific startup errors due to differences in API protocol standards (OpenAI format vs. Anthropic format) and parameter limitations. The following covers the most frequent errors and their fix solutions during the integration process of Chinese large models.

Core Error Code Reference Table

Configuration File Integrity Verification

After modifying ~/.openclaw/openclaw.json, directly restarting the service may cause a crash due to missing punctuation marks. It is recommended to verify the legitimacy of the JSON syntax via command line tools before restarting:

# Use jq to verify configuration file format (no output means format is correct)
cat ~/.openclaw/openclaw.json | jq empty

# If the format is correct, then execute restart
openclaw gateway restart

Additionally, please note that the "reasoning capability" and "context window" of Chinese models are not always directly proportional. If the Agent frequently reports errors during long conversations, in addition to checking for Token overflow, you should also confirm whether the provider's Rate Limit has been triggered, which is particularly common when executing Agent loops concurrently.