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The storage module handles uploading generated llms.txt files to Cloudflare R2 (S3-compatible object storage). It uses boto3 for S3 API compatibility and generates deterministic filenames based on URL hashing.

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Overview

The storage module provides a single async function for saving llms.txt content to R2:
  • MD5-based filename generation for consistent paths
  • S3-compatible upload using boto3
  • Public URL generation with custom domain support
  • Graceful handling when storage is not configured

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Configuration

Storage requires the following environment variables (from config.settings):
  • r2_endpoint - R2 endpoint URL
  • r2_access_key - R2 access key ID
  • r2_secret_key - R2 secret access key
  • r2_bucket - Target bucket name
  • r2_public_domain - Optional custom domain for public URLs

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Functions

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save_llms_txt()

Saves llms.txt content to R2 storage and returns the public URL. 
async def save_llms_txt(
    base_url: str,
    content: str,
    log: Callable
) -> str | None
​
base_url
str
required
The base URL of the crawled site (used for filename generation)
​
content
str
required
The formatted llms.txt content to upload
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log
Callable
required
Logging function (sync or async) that accepts string messages
​
public_url
str | None
Public URL of the uploaded file, or None if upload failed or storage not configured
Behavior:
  1. Checks if all required R2 settings are configured
  2. If not configured, logs message and returns None
  3. Creates boto3 S3 client with R2 credentials
  4. Generates MD5 hash of base URL for deterministic filename
  5. Uploads content with text/markdown content type
  6. Constructs public URL using custom domain or endpoint
  7. Logs upload success with URL
  8. Returns public URL on success, None on error
Object Key Format: 
llms/<md5_hash>.txt
Where <md5_hash> is the MD5 hash of the base URL.

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Usage Examples

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Basic Upload

from backend.storage import save_llms_txt

async def log(msg: str):
    print(msg)

base_url = "https://example.com"
content = "# Example Site\n\n> Description\n\n## Docs\n..."

public_url = await save_llms_txt(base_url, content, log)

if public_url:
    print(f"Uploaded to: {public_url}")
else:
    print("Upload failed or storage not configured")

​
Integration with Crawler and Formatter

from backend.crawler import LLMCrawler
from backend.formatter import format_llms_txt, get_md_url_map
from backend.storage import save_llms_txt

async def log(msg: str):
    print(msg)

# Crawl site
crawler = LLMCrawler(
    base_url="https://docs.example.com",
    max_pages=50,
    desc_length=150,
    log_callback=log
)

pages = await crawler.run()

# Format output
md_url_map = await get_md_url_map(pages)
llms_content = format_llms_txt(
    base_url="https://docs.example.com",
    pages=pages,
    md_url_map=md_url_map
)

# Upload to R2
public_url = await save_llms_txt(
    base_url="https://docs.example.com",
    content=llms_content,
    log=log
)

if public_url:
    await log(f"✓ llms.txt available at: {public_url}")

​
Without Storage Configured

# If R2 credentials are not set in environment
public_url = await save_llms_txt(base_url, content, log)

# Logs: "Storage not configured, skipping upload"
# Returns: None

​
Error Handling

from botocore.exceptions import ClientError

public_url = await save_llms_txt(base_url, content, log)

if public_url is None:
    # Either not configured or upload failed
    # Check logs for error details
    await log("Failed to upload - check R2 credentials")
else:
    # Success
    await log(f"Upload successful: {public_url}")

​
Public URL Generation

The function generates public URLs in two ways:

​
With Custom Domain

If settings.r2_public_domain is set: 
public_url = f"{settings.r2_public_domain}/{object_key}"
# Example: https://cdn.example.com/llms/5d41402abc4b2a76b9719d911017c592.txt

​
Without Custom Domain

Falls back to R2 endpoint: 
public_url = f"{settings.r2_endpoint}/{settings.r2_bucket}/{object_key}"
# Example: https://r2.example.com/mybucket/llms/5d41402abc4b2a76b9719d911017c592.txt

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Filename Determinism

The MD5 hash ensures consistent filenames for the same base URL: 
import hashlib

base_url = "https://example.com"
url_hash = hashlib.md5(base_url.encode()).hexdigest()
object_key = f"llms/{url_hash}.txt"

# Same base_url always produces same object_key
# This allows overwriting previous versions automatically
Benefits:
  • No need to track or delete old files
  • Recrawls automatically update the same file
  • Predictable URLs for integration

​
Content Type

Files are uploaded with Content-Type: text/markdown: 
s3_client.put_object(
    Bucket=settings.r2_bucket,
    Key=object_key,
    Body=content.encode(),
    ContentType='text/markdown'  # Enables proper rendering
)
This ensures browsers and LLM tools recognize the content as markdown.

​
Error Handling

The function handles errors gracefully: 
try:
    # Upload logic
    return public_url
except ClientError as e:
    log(f"Storage error: {str(e)}")
    return None
Common Errors:
  • Invalid credentials → ClientError
  • Network timeout → ClientError
  • Bucket not found → ClientError
  • Permission denied → ClientError
All errors are logged and return None.

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Dependencies

  • boto3 - AWS SDK for S3-compatible operations
  • botocore - Error handling for boto3
  • config - Settings management
  • formatter - Generates the content to upload
  • database - Stores the public URL in Supabase
  • config - Provides R2 configuration settings

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Notes

  • Function is async for consistency with other backend modules
  • Boto3 operations are synchronous (no async boto3 used)
  • Graceful degradation when storage not configured
  • Public URLs are immediately accessible after upload
  • No explicit public ACL needed (bucket configured for public reads)

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