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Agent Crashes on Unicode, Emoji, or Non-ASCII Input

Symptom

• UnicodeDecodeError: 'ascii' codec can't decode byte 0xf0 in position 3
• UnicodeEncodeError: 'latin-1' codec can't encode character '\U0001f600'
• Emoji in user input replaced with ? or \ufffd in the output
• MySQL/Postgres error: Incorrect string value: '\xF0\x9F\x98\x80' (emoji not stored)
• JSON serialization fails: json.dumps() on string with surrogate characters
• Log file shows \u0000 or corrupted bytes where emoji should be

Root Cause

Python 2 legacy code, open() without encoding="utf-8", databases configured for latin1 instead of utf8mb4, or json.dumps() without ensure_ascii=False all cause encoding failures. Emoji require 4-byte UTF-8 sequences (U+1F000 and above). MySQL’s utf8 charset only supports 3-byte sequences — emoji need utf8mb4. Python’s default ascii codec rejects anything above U+007F.

Fix

Option 1: Always open files with explicit UTF-8 encoding

import io

# WRONG — uses platform default encoding (often not UTF-8 on Windows)
with open("output.txt", "w") as f:
    f.write("Hello 🌍")  # May crash or garble

# RIGHT — explicit UTF-8 always
with open("output.txt", "w", encoding="utf-8") as f:
    f.write("Hello 🌍")

# RIGHT — binary mode with explicit encode/decode
with open("output.txt", "wb") as f:
    f.write("Hello 🌍".encode("utf-8"))

# Set default encoding for all file operations in your process:
import sys
# Check current default:
print(sys.getdefaultencoding())  # Should be 'utf-8'
# If not utf-8, set PYTHONIOENCODING=utf-8 environment variable

# Read files defensively:
def read_file_safe(path: str) -> str:
    """Read file with UTF-8, falling back to latin-1 for legacy files"""
    try:
        return open(path, encoding="utf-8").read()
    except UnicodeDecodeError:
        print(f"Warning: {path} is not UTF-8, trying latin-1")
        return open(path, encoding="latin-1").read()

Option 2: Sanitize user input for encoding issues

import unicodedata
import re

def normalize_unicode(text: str, mode: str = "NFC") -> str:
    """
    Normalize Unicode to a consistent form.
    NFC: composed form (preferred for storage)
    NFKC: compatibility decomposition (useful for search/comparison)
    """
    return unicodedata.normalize(mode, text)

def sanitize_text_input(text: str, allow_emoji: bool = True) -> str:
    """
    Sanitize text for safe processing and storage.
    """
    if not isinstance(text, str):
        text = str(text)

    # Normalize Unicode
    text = normalize_unicode(text)

    # Remove null bytes (cause issues in C-based libraries and databases)
    text = text.replace("\x00", "")

    # Remove or replace surrogates (broken Unicode from bad decoding)
    text = text.encode("utf-8", errors="surrogatepass").decode("utf-8", errors="replace")

    if not allow_emoji:
        # Remove emoji (characters outside BMP + emoji blocks)
        text = re.sub(
            r'[\U0001F000-\U0001FFFF\U0002F000-\U0002FFFF]',
            '',
            text
        )

    return text.strip()

# Usage:
user_message = sanitize_text_input(raw_user_input)

Option 3: JSON with proper Unicode handling

import json

text_with_emoji = "Hello 🌍! Привет! 日本語"

# WRONG — escapes all non-ASCII to \uXXXX sequences
json.dumps({"text": text_with_emoji})
# → '{"text": "Hello \\ud83c\\udf0d! \\u041f\\u0440\\u0438..."}'

# RIGHT — keep Unicode as-is (smaller, human-readable)
json.dumps({"text": text_with_emoji}, ensure_ascii=False)
# → '{"text": "Hello 🌍! Привет! 日本語"}'

# For API responses, always use ensure_ascii=False:
def safe_json_dumps(obj, **kwargs) -> str:
    return json.dumps(obj, ensure_ascii=False, **kwargs)

def safe_json_loads(text: str) -> dict:
    """Parse JSON, handling BOM and encoding issues"""
    # Remove BOM if present
    text = text.lstrip("\ufeff")
    try:
        return json.loads(text)
    except json.JSONDecodeError as e:
        # Try re-encoding as UTF-8 if parsing fails
        if isinstance(text, bytes):
            text = text.decode("utf-8", errors="replace")
        return json.loads(text.encode("utf-8").decode("utf-8"))

Option 4: Database emoji support (MySQL utf8mb4)

import pymysql

# WRONG — MySQL 'utf8' only supports 3-byte UTF-8, not emoji
connection = pymysql.connect(
    host="localhost",
    db="mydb",
    charset="utf8"  # Can't store emoji!
)

# RIGHT — utf8mb4 supports full Unicode including emoji
connection = pymysql.connect(
    host="localhost",
    db="mydb",
    charset="utf8mb4",
    use_unicode=True
)

# Also run in MySQL:
# ALTER DATABASE mydb CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
# ALTER TABLE messages CONVERT TO CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;

# PostgreSQL (already full UTF-8 — just ensure encoding):
import psycopg2
conn = psycopg2.connect(
    dsn="postgresql://localhost/mydb",
    # PostgreSQL uses UTF-8 by default — no special charset needed
)

# SQLite (UTF-8 by default — no configuration needed)
import sqlite3
conn = sqlite3.connect("data.db")
# → Works with all Unicode including emoji out of the box

Option 5: Detect encoding of incoming bytes

import chardet

def decode_bytes_safely(data: bytes) -> str:
    """
    Decode bytes to string, detecting encoding if unknown.
    Priority: UTF-8 → BOM detection → chardet → latin-1 fallback.
    """
    # 1. Try UTF-8 first (most common)
    try:
        return data.decode("utf-8")
    except UnicodeDecodeError:
        pass

    # 2. Check for BOM (byte order mark)
    if data.startswith(b"\xff\xfe"):
        return data.decode("utf-16-le")
    if data.startswith(b"\xfe\xff"):
        return data.decode("utf-16-be")
    if data.startswith(b"\xef\xbb\xbf"):
        return data[3:].decode("utf-8")

    # 3. Use chardet to detect encoding
    detected = chardet.detect(data)
    if detected["confidence"] > 0.7 and detected["encoding"]:
        try:
            return data.decode(detected["encoding"])
        except (UnicodeDecodeError, LookupError):
            pass

    # 4. Fallback: latin-1 never fails (every byte is valid)
    print(f"Warning: encoding unknown, falling back to latin-1")
    return data.decode("latin-1")

# Reading files from external sources:
with open("unknown_encoding_file.txt", "rb") as f:
    raw_bytes = f.read()
text = decode_bytes_safely(raw_bytes)

Option 6: Log and HTTP response encoding

import logging
import sys

# Ensure stdout/stderr use UTF-8 (critical on Windows)
if sys.platform == "win32":
    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
    sys.stderr.reconfigure(encoding="utf-8", errors="replace")

# Logging handler with explicit UTF-8
handler = logging.FileHandler("agent.log", encoding="utf-8")
handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s: %(message)s"))
logging.getLogger().addHandler(handler)

# FastAPI/Flask response encoding:
from fastapi import FastAPI
from fastapi.responses import JSONResponse

app = FastAPI()

@app.get("/result")
async def get_result() -> JSONResponse:
    data = {"message": "Hello 🌍! こんにちは!"}
    # JSONResponse uses json.dumps with ensure_ascii=False by default in FastAPI
    return JSONResponse(content=data, media_type="application/json; charset=utf-8")

# Requests library — always returns str (UTF-8 decoded):
import requests
response = requests.get("https://api.example.com/data")
response.encoding = "utf-8"  # Force UTF-8 if server doesn't specify
text = response.text  # Always str, decoded correctly

Encoding Error Reference

Error	Cause	Fix
UnicodeDecodeError: 'ascii'	open() without encoding=	Add encoding="utf-8"
UnicodeEncodeError: 'latin-1'	Writing non-Latin chars to latin-1 stream	Use encoding="utf-8"
Incorrect string value: '\xF0...'	MySQL with utf8 charset (not utf8mb4)	Change table charset to utf8mb4
\ufffd replacement character	Decoding with wrong codec	Detect encoding with chardet first
JSON \u0000 in string	Null bytes in text	Strip nulls: text.replace("\x00", "")
Surrogate characters	Broken decode → re-encode	errors="surrogatepass" then re-encode

Expected Token Savings

Unicode crash mid-task → restart → debug → fix: ~12,000 tokens Defensive encoding at input boundaries: 0 crashes

Environment

• Any agent processing user-generated content, multilingual text, or social media data; critical for consumer-facing agents
• Source: direct experience; encoding errors are the most common crash for agents moving from English-only dev to international production

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