agent-debugger（Agent 调试器）

Agent Debugger

Systematic debugging for AI agent issues. When your agent misbehaves, this skill helps identify and fix the problem.

Common Agent Problems

1. Infinite Loops

Symptoms:

• Agent repeats same action
• Gets stuck in a pattern
• Never completes task

Diagnosis:

Agent log shows:
- Same tool called 10+ times
- Same output format repeated
- No progress between iterations

Fixes:

Add iteration limit:

{
  "maxIterations": 5,
  "onLimit": "ask_user"
}

Add explicit stop condition:

In your instructions, add:
"If you've tried the same approach 3 times without success, stop and ask the user for guidance."

2. Tool Failures

Symptoms:

• Tool returns error
• Tool times out
• Tool not found

Diagnosis:

Check:
- Tool exists in available_tools
- Parameters match tool schema
- Tool has required permissions
- Rate limits not exceeded

Fixes:

Validate parameters first:

# Before calling tool
required_params = tool.get("required", [])
for param in required_params:
    if param not in args:
        raise ValueError(f"Missing required parameter: {param}")

Add retry logic:

{
  "retries": 3,
  "retryDelay": 1000,
  "retryOn": ["rate_limit", "timeout", "5xx"]
}

3. Context Overflow

Symptoms:

• "Context length exceeded" error
• Agent forgets earlier conversation
• Truncated outputs

Diagnosis:

Check context window:
- Current tokens vs max tokens
- Number of messages in history
- Size of file contents loaded

Fixes:

Use memory efficiently:

- Load only relevant files
- Use offset/limit for large files
- Summarize long conversations
- Clear old context periodically

Compress context:

# Instead of full file
content = read("file.txt", offset=1, limit=100)

# Use memory_search for specific info
results = memory_search("important decision")

4. Rate Limiting

Symptoms:

• "Rate limit exceeded" error
• Requests blocked
• 429 status codes

Diagnosis:

Check:
- API rate limits (requests per minute/hour)
- Token limits (tokens per minute)
- Concurrent request limits
- Time until reset

Fixes:

Add backoff:

import time
import random

def call_with_backoff(func, max_retries=5):
    for attempt in range(max_retries):
        try:
            return func()
        except RateLimitError as e:
            wait = (2 ** attempt) + random.random()
            time.sleep(wait)
    raise Exception("Max retries exceeded")

Queue requests:

from queue import Queue
from threading import Thread

request_queue = Queue()

def process_queue():
    while True:
        task = request_queue.get()
        result = execute(task)
        request_queue.task_done()
        time.sleep(0.1)  # Rate limit: 10 req/s

5. Memory Issues

Symptoms:

• Agent doesn't remember previous context
• MEMORY.md not loaded
• Memory files not found

Diagnosis:

Check:
- MEMORY.md exists
- memory/ directory exists
- Files have correct permissions
- Memory loaded at startup

Fixes:

Verify memory setup:

ls -la ~/.openclaw/workspace/
# Should show:
# MEMORY.md
# memory/

Add memory to instructions:

Before answering anything about prior work, decisions, dates, people, or todos: 
run memory_search on MEMORY.md + memory/*.md

6. Permission Errors

Symptoms:

• "Permission denied"
• "Access denied"
• Tools not working

Diagnosis:

Check:
- User permissions
- File permissions
- Tool policies
- Sandbox restrictions

Fixes:

Check file permissions:

ls -la /path/to/file
chmod 600 ~/.openclaw/workspace/sensitive.json

Review tool policies:

{
  "tools": {
    "exec": {
      "security": "ask",  // or "allowlist" or "full"
      "ask": "on-miss"    // or "always" or "off"
    }
  }
}

7. Performance Issues

Symptoms:

• Slow responses
• Timeouts
• High resource usage

Diagnosis:

Profile the agent:
- Time each tool call
- Count tokens used
- Measure context growth
- Identify bottlenecks

Fixes:

Optimize context:

# Instead of loading entire file
content = read("large_file.txt", limit=50)

# Use targeted search
results = memory_search("specific topic")

Reduce tool calls:

# Bad: Multiple calls
file1 = read("file1.txt")
file2 = read("file2.txt")
file3 = read("file3.txt")

# Good: Parallel or combined
files = read(["file1.txt", "file2.txt", "file3.txt"])

Debugging Workflow

Step 1: Reproduce

1. Document exact steps to trigger issue
2. Note expected vs actual behavior
3. Check if issue is consistent or intermittent
4. Try with minimal example

Step 2: Isolate

1. Disable other skills
2. Reduce context to minimum
3. Simplify task
4. Test each component separately

Step 3: Diagnose

1. Check logs (if available)
2. Review tool outputs
3. Examine context window
4. Verify configuration

Step 4: Fix

1. Apply fix
2. Test fix
3. Document fix
4. Update instructions if needed

Step 5: Prevent

1. Add guardrails
2. Update error handling
3. Add logging
4. Document in memory

Debugging Tools

Check Agent Status

# If you have access to session tools
status = session_status()
print(f"Model: {status['model']}")
print(f"Tokens used: {status['usage']['total_tokens']}")
print(f"Reasoning: {status['reasoning']}")

Clear Context

If agent is stuck:
1. Start new session
2. Load only essential memory
3. Re-approach task fresh

Enable Verbose Mode

{
  "thinking": "verbose",
  "reasoning": "on"
}

This shows internal reasoning, helping identify where logic fails.

Common Error Messages

Best Practices

1. Defensive Coding

# Always check before acting
if not os.path.exists(file):
    return "File not found"

try:
    result = risky_operation()
except ExpectedError:
    handle_error()

2. Progress Tracking

In agent instructions:
"Track your progress. After each major step, note what you've done and what's next."

3. Checkpointing

For long tasks:
- Save progress periodically
- Document current state
- Allow resuming from checkpoint

4. Logging

# Add to critical operations
log(f"Starting operation: {operation}")
log(f"Parameters: {params}")
log(f"Result: {result}")
log(f"Error: {error}")

When to Ask for Help

Ask the user when:

• Multiple fix attempts failed
• Issue is intermittent
• Would require destructive actions
• Need information only user has
• Configuration changes needed

Prevention Tips

1. Set limits early - max iterations, max tokens, max retries
2. Validate inputs - check parameters before calling tools
3. Handle errors gracefully - don't crash, report and adapt
4. Log important events - helps debugging later
5. Test edge cases - empty inputs, large files, special characters
6. Monitor resources - tokens, time, memory usage
7. Document quirks - save lessons in MEMORY.md