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openclaw - 💡(How to fix) Fix Feature Request: Support automatic multi-model task routing and fallback [1 comments, 2 participants]

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openclaw/openclaw#50904•Fetched 2026-04-08 01:06:43
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I would like OpenClaw to support automatic model selection and routing based on task type or complexity, as well as a fallback mechanism in case the primary model fails. Currently, OpenClaw requires manually switching between agents for different models, which limits workflow efficiency in multi-model scenarios.

Error Message

There is no built-in automatic routing or fallback, so workflows involving multiple models are cumbersome, error-prone, and hard to scale.

Root Cause

I would like OpenClaw to support automatic model selection and routing based on task type or complexity, as well as a fallback mechanism in case the primary model fails. Currently, OpenClaw requires manually switching between agents for different models, which limits workflow efficiency in multi-model scenarios.

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Summary

Summary

I would like OpenClaw to support automatic model selection and routing based on task type or complexity, as well as a fallback mechanism in case the primary model fails. Currently, OpenClaw requires manually switching between agents for different models, which limits workflow efficiency in multi-model scenarios.

Problem to solve

User pain / current behavior:

OpenClaw currently requires manual agent switching to use different models. For example, a user must explicitly run /agent coder or /agent analysis to select a model.

There is no built-in automatic routing or fallback, so workflows involving multiple models are cumbersome, error-prone, and hard to scale.

Users cannot define a mapping of task type → model, or have a fallback model chain if the primary model fails.

Why current behavior is insufficient:

Manual switching introduces friction and slows down work, especially in batch or automated pipelines.

Teams using OpenClaw for mixed tasks (coding, analysis, Q&A) must repeatedly intervene, causing delays and extra effort.

There is no native way to optimize model usage according to task type or prompt complexity.

Proposed solution

Desired behavior / API / UX:

Allow configuration of task categories and map each to a model:

{ "agents": { "defaults": { "model": "gpt-5-medium" }, "routers": { "tasks": { "coding": { "model": "gpt-5.1-codex" }, "analysis": { "model": "gpt-5.2-high" } } }, "modelRouting": { "primary": "gpt-5-medium", "fallbacks": ["gpt-5.1-codex", "gpt-5.2-high"] } } }

Automatic routing: OpenClaw determines the proper model for a prompt based on the task type or prompt characteristics without user intervention.

Fallback support: If the primary model fails or is unavailable, the system automatically uses the next model in the fallback chain.

Backward compatibility: Existing single-agent workflows continue to work without changes.

UX / API expectations:

/agent <task> command optionally uses the automatic routing rules.

Users can optionally override the default model manually.

Configuration is declarative, stored in openclaw.json.

Alternatives considered

No response

Impact

Who is affected / Severity / Frequency / Consequences:

Affected users: Anyone running multi-task workflows that require multiple models, e.g., developers, data analysts, AI engineers.

Severity: Blocks workflow; currently, users must manually switch agents which disrupts automation.

Frequency: Always, for users who have multiple models in the same workflow.

Practical consequences:

Extra manual work to switch agents.

Potential errors when users forget to select the right model.

Delays in batch processing or automated pipelines.

Reduced productivity in engineering and AI-assisted tasks.

Overall: This feature would significantly improve efficiency, reduce errors, and make OpenClaw practical for complex, multi-model workflows.

Evidence/examples

No response

Additional information

No response

extent analysis

Fix Plan

To implement automatic model selection and routing based on task type or complexity, as well as a fallback mechanism, follow these steps:

  • Update the openclaw.json configuration file to include task categories and model mappings:
{
  "agents": {
    "defaults": { "model": "gpt-5-medium" },
    "routers": {
      "tasks": {
        "coding": { "model": "gpt-5.1-codex" },
        "analysis": { "model": "gpt-5.2-high" }
      }
    },
    "modelRouting": {
      "primary": "gpt-5-medium",
      "fallbacks": ["gpt-5.1-codex", "gpt-5.2-high"]
    }
  }
}
  • Modify the /agent command to optionally use automatic routing rules:
def agent_command(task):
    # Check if task type is specified
    if task in config['agents']['routers']['tasks']:
        # Use the mapped model for the task type
        model = config['agents']['routers']['tasks'][task]['model']
    else:
        # Use the primary model as fallback
        model = config['agents']['modelRouting']['primary']
    
    # Run the agent with the selected model
    run_agent(model)
  • Implement a fallback mechanism to use the next model in the chain if the primary model fails:
def run_agent(model):
    try:
        # Run the agent with the selected model
        # ...
    except ModelFailureError:
        # Get the fallback models
        fallbacks = config['agents']['modelRouting']['fallbacks']
        
        # Try each fallback model in order
        for fallback in fallbacks:
            try:
                # Run the agent with the fallback model
                run_agent(fallback)
                break
            except ModelFailureError:
                # Continue to the next fallback model
                continue

Verification

To verify that the fix worked, test the following scenarios:

  • Run the /agent command with a task type that has a mapped model (e.g. coding or analysis).
  • Run the /agent command without specifying a task type to use the primary model.
  • Simulate a model failure and verify that the fallback mechanism kicks in and uses the next model in the chain.

Extra Tips

  • Make sure to handle errors and exceptions properly to avoid disrupting the workflow.
  • Consider adding logging and monitoring to track model usage and fallback events.
  • Test the implementation thoroughly to ensure that it works as expected in different scenarios.

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openclaw - 💡(How to fix) Fix Feature Request: Support automatic multi-model task routing and fallback [1 comments, 2 participants]