惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

罗磊的独立博客
G
Google Developers Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
腾讯CDC
有赞技术团队
有赞技术团队
Vercel News
Vercel News
MongoDB | Blog
MongoDB | Blog
M
MIT News - Artificial intelligence
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
B
Blog RSS Feed
I
InfoQ
Blog — PlanetScale
Blog — PlanetScale
博客园_首页
The Cloudflare Blog
B
Blog
C
Check Point Blog
Stack Overflow Blog
Stack Overflow Blog
IT之家
IT之家
U
Unit 42
D
Docker
月光博客
月光博客
aimingoo的专栏
aimingoo的专栏
博客园 - Franky
A
About on SuperTechFans

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
LLM Memory System Pitfalls: A 3-Hour Bug Hunt Solved with...
BAOFUFAN · 2026-06-12 · via DEV Community

BAOFUFAN

It was 2 a.m. when the alert call jolted me awake — our production Agent had suffered “amnesia” for three consecutive conversations. The context the user had carefully built was gone, and complaints were flooding in. Squinting at the logs, I discovered that the rollback method in the memory management module had been broken by an innocuous-looking code refactor. Not only did the rollback undo the erroneous operation, it also wiped out the entire conversation history. Worse still, our existing unit tests never caught the bug: they always started from a fresh empty database and could never cover a cross-session scenario like “roll back dirty data to a previous snapshot.” I spent three hours debugging, manually simulating intermediate states, before I finally pinpointed the root cause. That’s when it hit me: we weren't lacking tests — we were missing snapshot tests that capture the entire “memory state.”


Problem Breakdown

Our LLM memory system uses SQLite for local persistence. Each session owns a table that stores conversation turns, vector summaries, and tool-call records. Two critical operations are:

  • save_snapshot(session_id): serializes the full state of a session into the snapshots table, creating a rollback checkpoint.
  • rollback_to_snapshot(session_id, snapshot_id): when something goes wrong, it rebuilds the session table from a snapshot and discards all changes made after that point.

This mechanism had been running smoothly — until a refactor I made changed the transaction boundaries inside the rollback logic. After the rollback executed, the conversations table was rebuilt just fine, but the snapshots table itself was accidentally wiped out. The next rollback attempt couldn’t find any previous checkpoints.

Why didn’t traditional unit tests catch this? Because the typical test flow looks like this:

def test_rollback():
    db = create_in_memory_db()
    db.save_snapshot("s1")
    db.rollback_to_snapshot("s1", ...)
    assert db.get_conversation("s1") == expected

Everything runs in a single process, inside a single temporary database. However, the production scenario was different: process A saves a snapshot and exits, then process B reopens the same database file and performs the rollback. File-level persistent state, WAL log merging, and even the visibility of the snapshots table across different connections — none of that was tested. To put it bluntly, we tested the “logic” but never tested the “storage.”


Solution Design

I decided to bring in snapshot testing, but instead of using text-based snapshots, I would treat the SQLite database file itself as an immutable artifact.

Comparison of approaches:

  • pytest-snapshot: only works with text/JSON snapshots, not suitable for binary or complex state comparisons.
  • pytest’s tmp_path + manual comparison: flexible, but writing comparison logic by hand every time easily misses fields.
  • File hash + in-database diffing: compute a sha256 hash of the entire database file as a holistic snapshot, and optionally extract key tables for a human-readable diff. This lets us quickly detect “what changed” while retaining fine-grained debugging capabilities.

The architectural idea: provide a snapshot_db fixture via conftest.py that:

  1. Checks whether a baseline snapshot file (e.g. tests/snapshots/memory_test.sqlite) exists before the test starts.
  2. If it doesn’t, auto-generates it (with the --snapshot-update flag) and the test passes immediately.
  3. If the baseline exists, after the test operations it computes the sha256 of the resulting database file and compares it to the baseline’s hash. If they differ, the test fails and outputs a diff hint.

With this approach, our tests truly simulate a “cross-process, cross-connection” persistence effect — each test case receives an independent copy of a database file, performs its operations, and then the entire file state is compared against the expected outcome.


Core Implementation

1. Build a Persistable Memory Manager

This code clarifies what we intend to test. MemoryManager wraps the SQLite connection, snapshot saving, and rollback — a simplified version of what we use in production.

# memory_manager.py
import sqlite3
import uuid
from datetime import datetime, timezone

class MemoryManager:
    def __init__(self, db_path: str):
        self.db_path = db_path
        self._init_tables()

    def _get_conn(self) -> sqlite3.Connection:
        conn = sqlite3.connect(self.db_path)
        conn.execute("PRAGMA journal_mode=WAL")
        conn.row_factory = sqlite3.Row
        return conn

    def _init_tables(self):
        with self._get_conn() as conn:
            conn.executescript("""
                CREATE TABLE IF NOT EXISTS conversations (
                    session_id TEXT NOT NULL,
                    turn INTEGER NOT NULL,
                    role TEXT NOT NULL,
                    content TEXT NOT NULL,
                    PRIMARY KEY (session_id, turn)
                );
                CREATE TABLE IF NOT EXISTS snapshots (
                    snapshot_id TEXT PRIMARY KEY,
                    session_id TEXT NOT NULL,
                    created_at TEXT NOT NULL,
                    state_json TEXT NOT NULL
                );
            """)

    def add_message(self, session_id: str, role: str, content: str):
        with self._get_conn() as conn:
            turn = conn.execute(
                "SELECT COALESCE(MAX(turn), 0) + 1 FROM conversations WHERE session_id = ?",