SQLite Database Errors with Multiple Agent Workers — Not Thread-Safe
Symptom
sqlite3.OperationalError: database is lockedProgrammingError: SQLite objects created in a thread can only be used in that same thread- Data disappears or is partially written under concurrent load
- Works with one worker, fails with multiple
- Error rate increases with number of concurrent workers
Root Cause
SQLite has a single-writer lock. Only one writer at a time, and the default lock timeout is very short. sqlite3.Connection objects are also not thread-safe — creating a connection in one thread and using it in another raises ProgrammingError. Many agent frameworks create one connection at startup and share it across workers.
Fix
Option 1: Create connection per thread/task (safest)
import sqlite3
import threading
# WRONG — shared connection across threads
shared_conn = sqlite3.connect("agent.db")
def worker(task):
shared_conn.execute("INSERT ...") # ProgrammingError in another thread!
# RIGHT — create connection per thread using threading.local
_local = threading.local()
def get_connection() -> sqlite3.Connection:
"""Get thread-local SQLite connection"""
if not hasattr(_local, "conn"):
_local.conn = sqlite3.connect(
"agent.db",
timeout=30, # Wait up to 30s for write lock
check_same_thread=True # Enforce thread safety
)
_local.conn.execute("PRAGMA journal_mode=WAL") # Better concurrent reads
return _local.conn
def worker(task):
conn = get_connection() # Gets THIS thread's connection
conn.execute("INSERT INTO tasks VALUES (?)", (task["id"],))
conn.commit()
Option 2: Enable WAL mode for better concurrency
import sqlite3
def create_optimized_db(path: str) -> sqlite3.Connection:
"""Configure SQLite for concurrent agent access"""
conn = sqlite3.connect(path, timeout=30, check_same_thread=False)
# WAL mode allows concurrent readers + one writer
conn.execute("PRAGMA journal_mode=WAL")
# Increase busy timeout (wait longer before giving up on lock)
conn.execute("PRAGMA busy_timeout=30000") # 30 seconds
# Synchronous mode balance (NORMAL = safer than OFF, faster than FULL)
conn.execute("PRAGMA synchronous=NORMAL")
conn.row_factory = sqlite3.Row # Named columns
return conn
Option 3: Serialize writes through a queue
import asyncio, sqlite3, queue, threading
from typing import Any
class SerializedSQLite:
"""Single-writer SQLite via write queue — thread-safe reads, serialized writes"""
def __init__(self, path: str):
self.path = path
self._write_queue = queue.Queue()
self._writer_thread = threading.Thread(target=self._write_loop, daemon=True)
self._writer_thread.start()
def _write_loop(self):
conn = sqlite3.connect(self.path)
conn.execute("PRAGMA journal_mode=WAL")
while True:
sql, params, result_future = self._write_queue.get()
if sql is None:
break
try:
cursor = conn.execute(sql, params or ())
conn.commit()
result_future.set_result(cursor.lastrowid)
except Exception as e:
result_future.set_exception(e)
async def write(self, sql: str, params: tuple = ()) -> int:
"""Thread-safe write — serialized through queue"""
loop = asyncio.get_event_loop()
future = loop.create_future()
self._write_queue.put((sql, params, future))
return await future
def read(self, sql: str, params: tuple = ()) -> list:
"""Thread-safe read — each call creates its own connection"""
conn = sqlite3.connect(self.path)
conn.execute("PRAGMA journal_mode=WAL")
result = conn.execute(sql, params).fetchall()
conn.close()
return result
db = SerializedSQLite("agent.db")
Option 4: Use SQLAlchemy with connection pool
from sqlalchemy import create_engine, text
from sqlalchemy.pool import StaticPool
# For SQLite with multiple threads
engine = create_engine(
"sqlite:///agent.db",
connect_args={
"timeout": 30,
"check_same_thread": False
},
# StaticPool: single connection, thread-safe via lock
poolclass=StaticPool,
pool_pre_ping=True
)
# Better: use SQLite with NullPool for per-request connections
from sqlalchemy.pool import NullPool
engine = create_engine(
"sqlite:///agent.db",
connect_args={"timeout": 30},
poolclass=NullPool # New connection each time — fully thread-safe
)
from sqlalchemy.orm import sessionmaker
SessionLocal = sessionmaker(bind=engine, autocommit=False, autoflush=False)
def get_db_session():
session = SessionLocal()
try:
yield session
session.commit()
except Exception:
session.rollback()
raise
finally:
session.close()
Option 5: Migrate to PostgreSQL for production multi-agent use
# SQLite is great for single-agent, development, and testing
# For multiple concurrent agents in production, use PostgreSQL
# asyncpg — async PostgreSQL client
import asyncpg, os
async def get_pg_pool() -> asyncpg.Pool:
return await asyncpg.create_pool(
dsn=os.environ["DATABASE_URL"],
min_size=5,
max_size=20 # Scale with number of workers
)
pool = None
async def init():
global pool
pool = await get_pg_pool()
async def write_task_result(task_id: str, result: dict):
async with pool.acquire() as conn:
await conn.execute(
"INSERT INTO task_results (task_id, result) VALUES ($1, $2)",
task_id, json.dumps(result)
)
SQLite Concurrency Limits
| Scenario | SQLite ok? | Notes |
|---|---|---|
| 1 agent, all operations | Yes | Default config fine |
| Multiple agents, read-heavy | Yes | WAL mode + per-thread connections |
| Multiple agents, write-heavy | Marginal | WAL + serialize writes |
| 10+ concurrent writers | No | Use PostgreSQL |
| Production multi-agent | No | Use PostgreSQL or MySQL |
Expected Token Savings
Debugging SQLite locking errors: ~4,000 tokens WAL mode + per-thread connections prevents most issues: 0 wasted
Environment
- Multi-worker agent pipelines using SQLite as shared state
- Source: direct experience; SQLite’s concurrency limitations are well-documented
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