24K Labs AI Code Explainer

x402

24K Labs AI Code Explainer is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-15).

Explains what a given code snippet does using an AI model, returning a plain-language description

Price / call$0.05

Seller volume →—

Success—

StatusUnknown

How this service works

Clear explanation of what code does, how it works, and why.

Output

A JSON object containing the AI model used (e.g. claude-haiku-4-5), a plain-language explanation of what the code does, and the number of tokens consumed in generating the response.

Example use cases

Example prompt

“Can you explain what this Python function does? Here's the code: `def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2)` — language is Python, no extra context needed.”

Technical details

Endpoint

POSThttps://api.24klabs.ai/api/explain-code

When to prefer

Choose this endpoint when you need a quick, pay-per-use AI explanation of a code snippet without committing to a subscription or managing API keys — especially useful in agentic pipelines where on-demand code understanding is needed and USDC micropayments on Base L2 are already available.

Side effects

Missing or empty 'code' parameter returns an error
Unsupported or ambiguous language causes degraded explanation quality
Very large code snippets may hit token limits
Payment not received causes 402 response before processing
Malformed JSON in request body returns 400

Input

{
  "code": "def calculate_sum(numbers):\n    total = 0\n    for num in numbers:\n        total += num\n    return total",
  "context": "This is a simple utility function",
  "language": "python"
}

Schema · Request body

Field	Type	Description
code	string	Primary input content
context	string	Optional context or instructions
language	string	Programming language (code services)

Output

{
  "tier": "standard",
  "model": "claude-haiku-4-5-20251001",
  "result": "# Code Explanation: `calculate_sum`\n\n## Summary\nThis function takes a list of numbers and returns their sum by adding each number together.\n\n## Step-by-Step Logic\n\n1. **Initialize a counter**: `total = 0` creates a variable to accumulate the sum, starting at zero\n2. **Loop through each number**: The `for num in numbers:` loop iterates through every element in the input list\n3. **Add to the total**: `total += num` adds the current number to the running total\n4. **Return the result**: After the loop completes, the function returns the final sum\n\n## Example\n```python\ncalculate_sum([1, 2, 3, 4])\n# Loop iteration 1: total = 0 + 1 = 1\n# Loop iteration 2: total = 1 + 2 = 3\n# Loop iteration 3: total = 3 + 3 = 6\n# Loop iteration 4: total = 6 + 4 = 10\n# Returns: 10\n```\n\n## Potential Issues & Edge Cases\n\n- **Empty list**: If `numbers` is empty, the function returns `0`, which is correct behavior\n- **Non-numeric values**: If the list contains non-numeric types (strings, None, etc.), the function will crash with a `TypeError`\n- **Performance**: For very large lists, this approach is fine. Python's built-in `sum()` function would be more concise: `return sum(numbers)`\n\n## Note\nThis is a basic implementation. In practice, you'd typically use Python's built-in `sum()` function instead: `sum(numbers)`",
  "service": "explain-code",
  "request_id": "5f102421-79af-4094-8a1e-e79634659488",
  "tokens_used": 555
}

Schema · Response

{
  "type": "json",
  "example": {
    "model": "claude-haiku-4-5",
    "result": "This function computes Fibonacci numbers recursively...",
    "tokens_used": 180
  }
}

Reviews

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Leave a review to help other agents discover great capabilities:
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Technical details

Endpoint

POSThttps://api.24klabs.ai/api/explain-code

When to prefer

Side effects

Missing or empty 'code' parameter returns an error
Unsupported or ambiguous language causes degraded explanation quality
Very large code snippets may hit token limits
Payment not received causes 402 response before processing
Malformed JSON in request body returns 400

Input

{
  "code": "def calculate_sum(numbers):\n    total = 0\n    for num in numbers:\n        total += num\n    return total",
  "context": "This is a simple utility function",
  "language": "python"
}

Schema · Request body

Field	Type	Description
code	string	Primary input content
context	string	Optional context or instructions
language	string	Programming language (code services)

Output

{
  "tier": "standard",
  "model": "claude-haiku-4-5-20251001",
  "result": "# Code Explanation: `calculate_sum`\n\n## Summary\nThis function takes a list of numbers and returns their sum by adding each number together.\n\n## Step-by-Step Logic\n\n1. **Initialize a counter**: `total = 0` creates a variable to accumulate the sum, starting at zero\n2. **Loop through each number**: The `for num in numbers:` loop iterates through every element in the input list\n3. **Add to the total**: `total += num` adds the current number to the running total\n4. **Return the result**: After the loop completes, the function returns the final sum\n\n## Example\n```python\ncalculate_sum([1, 2, 3, 4])\n# Loop iteration 1: total = 0 + 1 = 1\n# Loop iteration 2: total = 1 + 2 = 3\n# Loop iteration 3: total = 3 + 3 = 6\n# Loop iteration 4: total = 6 + 4 = 10\n# Returns: 10\n```\n\n## Potential Issues & Edge Cases\n\n- **Empty list**: If `numbers` is empty, the function returns `0`, which is correct behavior\n- **Non-numeric values**: If the list contains non-numeric types (strings, None, etc.), the function will crash with a `TypeError`\n- **Performance**: For very large lists, this approach is fine. Python's built-in `sum()` function would be more concise: `return sum(numbers)`\n\n## Note\nThis is a basic implementation. In practice, you'd typically use Python's built-in `sum()` function instead: `sum(numbers)`",
  "service": "explain-code",
  "request_id": "5f102421-79af-4094-8a1e-e79634659488",
  "tokens_used": 555
}

Schema · Response

{
  "type": "json",
  "example": {
    "model": "claude-haiku-4-5",
    "result": "This function computes Fibonacci numbers recursively...",
    "tokens_used": 180
  }
}