Fix OpenClaw Responses That Are Too Short

If you've been working with OpenClaw and your agent keeps giving you these pathetic one-liner responses — "Yes." or "The answer is 42." or some equally unhelpful snippet — you're not alone. This is probably the single most common complaint I see from people getting started with OpenClaw agents, and the good news is it's completely fixable. The bad news is there are like six different things that could be causing it, and most guides only cover one.

Let's go through all of them.

Why Your OpenClaw Agent Sounds Like a Bored Teenager

First, let's understand what's actually happening when your agent returns a response that's too short. OpenClaw agents don't just "talk" — they go through a reasoning pipeline. They plan, they select tools, they generate output, and they format a final response. At every single one of those stages, there's a configuration setting or prompt instruction that can accidentally tell the agent to keep things brief.

The result? You ask your agent to "write a detailed summary of our Q3 sales performance," and it comes back with: "Q3 sales were $2.1M, up 12% from Q2."

Technically correct. Practically useless.

Here's what's going on under the hood and how to fix each cause.

Fix #1: Adjust the max_tokens Output Parameter

This is the most obvious one, but I'm amazed how many people miss it. OpenClaw respects a max_tokens parameter on the underlying model call, and if you've set it low (or left it at a conservative default), the model will literally stop generating mid-thought once it hits that ceiling.

agent = OpenClawAgent(
    model="gpt-4",
    max_output_tokens=256  # This is your problem right here
)

A max_output_tokens of 256 is roughly one short paragraph. If you want detailed, substantive responses, you need to crank this up significantly.

agent = OpenClawAgent(
    model="gpt-4",
    max_output_tokens=2048  # Much better for detailed responses
)

For most use cases, I recommend somewhere between 1024 and 4096 depending on what you're building. A customer support agent answering quick questions? 1024 is probably fine. A research agent writing reports? Go 4096 or higher.

One thing to keep in mind: higher max_output_tokens doesn't mean the agent will use all those tokens — it just means it can. You're setting a ceiling, not a floor. Which brings us to the next fix.

Fix #2: Set a Minimum Output Length

This is the one most people don't know about. OpenClaw has a min_response_length parameter that acts as a soft floor for response generation. If the agent's initial response is shorter than this threshold, OpenClaw will prompt it to expand.

agent = OpenClawAgent(
    model="gpt-4",
    max_output_tokens=4096,
    min_response_length=500,  # Minimum characters in final response
    expansion_prompt="Your response was too brief. Please provide more detail, examples, and explanation."
)

What's happening here is that OpenClaw checks the response length before returning it. If it's under 500 characters, it sends the response back to the model along with your expansion_prompt and asks it to elaborate. It's like having a built-in editor that says "this isn't enough, give me more."

You can customize the expansion prompt to be specific to your use case:

# For a technical documentation agent
expansion_prompt = """
Your response needs more detail. Please include:
- Step-by-step instructions
- Code examples where relevant  
- Common pitfalls to watch out for
- Expected output or results
"""

# For a customer support agent
expansion_prompt = """
Please expand your response to include:
- A clear explanation of the issue
- The specific steps to resolve it
- Links to relevant documentation
- An offer for further assistance
"""

This single parameter has probably fixed more "too short" complaints than anything else in OpenClaw.

Fix #3: Fix Your System Prompt

Here's where it gets less obvious. A lot of people write system prompts that accidentally encourage brevity. Look at your prompt and check if you have anything like this:

# BAD: These instructions encourage short responses
system_prompt = """
You are a helpful assistant. Be concise and efficient. 
Respond clearly and directly.
Don't waste the user's time.
"""

Every one of those instructions — "concise," "efficient," "directly," "don't waste time" — is telling the model to keep things short. The model is being obedient. You told it to be brief, and it listened.

Here's how to rewrite it:

# GOOD: These instructions encourage detailed responses
system_prompt = """
You are a knowledgeable assistant that provides thorough, detailed responses.

When answering questions:
- Provide comprehensive explanations, not just quick answers
- Include relevant examples, context, and background information
- Break complex topics into clear sections with headers
- Anticipate follow-up questions and address them proactively
- When providing instructions, include every step — don't skip "obvious" ones

Your responses should be detailed enough that the reader doesn't need to ask follow-up questions. Aim for completeness over brevity.
"""

See the difference? You're explicitly telling the agent what "good" looks like, and "good" means detailed and thorough.

One pro tip here: include an example of the response length and format you want directly in the system prompt. Models respond really well to examples.

system_prompt = """
You are a knowledgeable assistant that provides thorough responses.

Example of the level of detail expected:

User: "How do I set up error handling?"
Good response: "Error handling in OpenClaw involves three layers... [300+ words with code examples, edge cases, and recommendations]"
Bad response: "Use try/except blocks and set retry_on_error=3."

Always aim for the 'Good response' level of detail.
"""

Fix #4: Use the Plan-Then-Execute Strategy

If you're using OpenClaw's default execution strategy, the agent might be taking shortcuts. Switch to plan_then_execute mode, which forces the agent to outline what it's going to do before doing it. This naturally produces longer, more structured output.

agent = OpenClawAgent(
    model="gpt-4",
    strategy="plan_then_execute",
    plan_detail_level="high",  # Forces detailed planning
    show_reasoning=True
)

result = agent.execute("Explain our refund policy to the customer")

# The agent will now:
# [PLANNING] Breaking down the task...
# [PLAN] Step 1: Retrieve refund policy from knowledge base
# [PLAN] Step 2: Identify key points relevant to customer's situation
# [PLAN] Step 3: Explain policy in friendly, comprehensive language
# [PLAN] Step 4: Include specific timeframes, conditions, and next steps
# [PLAN] Step 5: Offer additional assistance
# [EXECUTING] Running plan...

The plan_detail_level="high" setting is key here. It tells the planning stage to be granular, which cascades into a more detailed final response. When the agent plans five specific steps, it tends to actually execute all five — versus the default mode where it might collapse everything into one quick answer.

Fix #5: Configure the Response Formatting Template

OpenClaw supports response templates that give the agent a structure to fill in. This is incredibly powerful for ensuring consistent, detailed output. Instead of letting the agent decide how much to write, you give it a framework.

response_template = """
## Summary
{Provide a 2-3 sentence overview}

## Detailed Explanation
{Provide a thorough explanation of the topic, minimum 3 paragraphs}

## Key Points
{List at least 5 important points with brief explanations for each}

## Examples
{Provide at least 2 concrete examples}

## Next Steps
{Recommend specific actions the reader should take}
"""

agent = OpenClawAgent(
    model="gpt-4",
    response_template=response_template,
    enforce_template=True  # Agent must fill all sections
)

When enforce_template is set to True, OpenClaw validates that every section in the template has been filled. If the agent skips a section or leaves one too sparse, it gets sent back for expansion. This is like handing someone a form with required fields — they can't submit it half-empty.

You can create different templates for different use cases:

# Template for technical questions
tech_template = """
## Answer
{Direct answer to the question}

## How It Works
{Technical explanation with relevant details}

## Implementation
{Code example with comments}

## Common Issues
{At least 3 potential problems and their solutions}

## Related Topics
{Links to related concepts the reader might find useful}
"""

# Template for customer-facing responses
support_template = """
## Understanding Your Issue
{Restate the customer's problem to show understanding}

## Solution
{Step-by-step resolution with clear instructions}

## Why This Happened
{Brief explanation of the root cause}

## Prevention
{How to avoid this issue in the future}

## Need More Help?
{Offer for additional assistance and relevant resources}
"""

Fix #6: Check Your Temperature Setting

This one's subtle. A very low temperature setting (close to 0) makes the model more deterministic and focused, which often means shorter responses. The model picks the single most likely token at each step, and the most likely response is often the most concise one.

# Very low temperature = more concise, less elaborate
agent = OpenClawAgent(
    model="gpt-4",
    temperature=0.1  # Very focused, often very terse
)

# Slightly higher temperature = more elaborate, more detailed
agent = OpenClawAgent(
    model="gpt-4",
    temperature=0.7  # More creative and expansive
)

I'm not saying crank your temperature to 1.0 — that'll get you longer responses, but they might also be rambling and less accurate. For most people, somewhere between 0.4 and 0.7 is the sweet spot. You get enough "creativity" that the model elaborates naturally, without going off the rails.

Putting It All Together

Here's the full configuration I recommend for agents that need to produce detailed, substantive output:

agent = OpenClawAgent(
    model="gpt-4",
    
    # Output length controls
    max_output_tokens=4096,
    min_response_length=500,
    expansion_prompt="Please provide more detail, examples, and step-by-step guidance.",
    
    # Execution strategy
    strategy="plan_then_execute",
    plan_detail_level="high",
    show_reasoning=True,
    
    # Response formatting
    response_template=your_template_here,
    enforce_template=True,
    
    # Generation settings
    temperature=0.6,
    
    # Safety controls (always include these)
    max_iterations=15,
    cost_limit_usd=2.00,
    retry_on_error=3,
    checkpoint=True,
    
    # System prompt that encourages detail
    system_prompt="""
    You are a thorough, knowledgeable assistant. Always provide 
    comprehensive responses with examples, context, and actionable 
    steps. Err on the side of too much detail rather than too little.
    """
)

This configuration hits every lever we talked about. You've raised the ceiling (max_output_tokens), set a floor (min_response_length), structured the output (template), told the agent to be detailed (system prompt), given it room to elaborate (temperature), and made it plan before executing (strategy).

The Quick Fix If You Don't Want to Configure All This

Look, I just walked you through six different things to tune. If you're looking at that and thinking "I just want my agent to give useful answers without me becoming an OpenClaw configuration expert," I get it.

Felix's OpenClaw Starter Pack on Claw Mart includes pre-configured skills that have all of this dialed in already. It's $29 and it comes with response length configurations, system prompts, and templates that have been tested and refined. The "detailed response" skill preset specifically solves the terse response problem out of the box — you just plug it in and your agent starts producing the kind of thorough, useful output we've been talking about. If you don't want to manually tune all six settings we just covered, it's genuinely the fastest path to getting this right.

What Not to Do

A few common mistakes I see people make when trying to fix short responses:

Don't just repeat "be more detailed" in every message. If your system prompt says "be detailed" and then every user message also says "please provide a detailed response," the model sometimes interprets this as a sign that something is wrong and actually becomes more cautious (and shorter) in its output. Set the instruction once, clearly, in the system prompt and let the agent do its thing.

Don't set max_output_tokens to the absolute maximum. Setting it to 128,000 because "more is better" will cost you money and won't produce better results. The agent won't use tokens it doesn't need. All you're doing is removing the safety net that prevents runaway costs. Be deliberate.

Don't confuse length with quality. The goal isn't to make your agent write essays. The goal is to make it write complete responses that actually answer the question. Sometimes a 200-word response is perfect. The problem is when an agent gives you 20 words for something that needs 200.

Final Thoughts

Short responses from OpenClaw agents are almost always a configuration issue, not a fundamental limitation. The platform gives you fine-grained control over output length at multiple levels — model parameters, execution strategy, response templates, system prompts, and post-generation validation. The trick is knowing which levers to pull.

Start with the system prompt and min_response_length — those two alone fix the problem for about 80% of people. If you need more control, add response templates and the plan-then-execute strategy. And if you want the whole thing pre-configured and battle-tested, grab the starter pack I mentioned and skip the trial-and-error phase entirely.

Your agent has plenty to say. You just need to let it.