DEV Community: Sunday Victor

How Do AI Agents Remember Things Between Conversations?

Sunday Victor — Sat, 27 Jun 2026 19:41:08 +0000

AI agents do not truly remember like humans do. Instead, they store useful information outside the chat, retrieve it later, and inject it back into the model when needed. That is what makes an agent feel continuous across sessions instead of resetting every time a conversation ends.

Why memory matters

Without memory, every chat starts from zero. The agent forgets your name, your preferences, your project context, and even decisions from previous sessions. That makes the experience repetitive, slower, and less useful.

Persistent memory changes that. It lets an agent remember important facts, personalize responses, and carry context forward over time. If you are building AI products, this is one of the clearest ways to make your agent feel smarter and more helpful.

What AI memory actually is

When people say an AI agent “remembers,” they usually mean one of two things.

First, the agent may remember the current conversation because the model still has access to recent messages. That is short-term memory, and it only lasts while the session is active.

Second, the agent may save important facts to an external memory system. That is long-term memory, and it can survive across sessions. On the next conversation, the system retrieves relevant memories and includes them in the prompt so the model can respond with context.

How agents remember between sessions

The process is simpler than it sounds:

The user says something important.

The system decides whether it should be saved.

The memory is stored outside the chat.

Later, when the user returns, the system searches for relevant memories.

The retrieved memories are added to the prompt.

The model answers using both the new message and the saved context.

That is how an agent can remember preferences, project details, names, and prior decisions without relying on a huge chat history every time.

What gets stored

Good memory systems are selective. They do not save every single message. They store only useful, durable information such as:

User preferences.

Names and identity details.

Locations.

Recurring goals.

Project decisions.

Corrections to earlier facts.

Important session summaries.

If you store too much, the memory becomes noisy and harder to trust. If you store too little, the agent feels forgetful. The goal is to keep only the context that will matter later.

Short-term vs long-term memory

Short-term memory keeps the current conversation coherent while the session is active. It helps the model understand what was just said and reply naturally.

Long-term memory is what survives across sessions. It helps the agent remember the user tomorrow, next week, or next month.

A strong AI product usually needs both. Short-term memory handles the live conversation. Long-term memory creates continuity.

Why retrieval is the real challenge

Memory is not just about saving data. It is about retrieving the right data at the right time.

If the wrong memory comes back, the agent can become confused or overly personalized. If no useful memory comes back, the agent acts like it forgot everything. The best systems rank memories by a mix of semantic relevance, recency, and importance so the most useful ones appear first.

That is what separates a useful memory layer from a simple storage layer.

Common failure points

AI memory often fails in predictable ways:

It stores stale information and never updates it.

It saves duplicates over and over.

It retrieves too many unrelated memories.

It has no easy way to delete or correct old facts.

It gives no visibility into what was stored.

That is why production memory needs lifecycle controls, not just retrieval. You need ways to update, deduplicate, inspect, and delete memory when facts change.

A simple example

Imagine a user says, “I prefer short answers.”

A good agent should store that preference. The next time the user returns, the system retrieves that memory and injects it into the prompt. The result is a reply that is automatically short, without the user needing to repeat themselves.

That is the real value of persistent memory: the agent feels like it knows the user.

How to add memory to your agent

If you are building an AI agent, you usually need:

A place to store memory.

A way to represent it, often with embeddings or structured fields.

A search layer to retrieve relevant memories.

A way to inject those memories into the prompt.

Controls for update, delete, and duplicate handling.

You can build this yourself, but it takes time to get right. A memory API can reduce the complexity and give you a cleaner system to work with.

Why Remem is useful here

If you want a simple way to give your AI agent persistent memory, Remem can help. It is a memory API for AI agents that stores what your agent learns, retrieves useful context later, and ranks memories by semantic relevance, recency, and importance.

That means your agent can remember the right things across sessions without forcing you to build the whole memory layer from scratch.

Final thoughts

AI agents remember things between conversations by storing useful information outside the chat and retrieving it later when needed. That is what gives them continuity, personalization, and better user experience.

If you are building an AI product, persistent memory is not just a nice feature. It is one of the foundations of making an agent feel real.

Call to action

If you are building a LangGraph agent or any AI app that needs continuity, try adding a memory layer early. A good memory system can turn a forgetful chatbot into a helpful, personalized assistant that actually improves over time.

Try Remem if you want a simple way to give your agent persistent memory without building the infrastructure yourself.
Remem — Persistent Memory API for AI Agents

I Built a Medium Article Scraper for Content Analysis & Research.

Sunday Victor — Mon, 22 Dec 2025 06:37:11 +0000

After spending hours manually collecting Medium articles for my research project, I decided to automate the process. Today, I'm sharing my Medium Article Scraper built on the Apify platform.

🎯 What Does It Do?

The scraper extracts comprehensive data from Medium articles:

Article Content: Full text, title, and subtitle
Author Information: Writer name and profile
Metadata: Publication date, reading time
Engagement: Response counts (comments)
Export Options: JSON and CSV formats

💡 Why I Built This

As a developer working on content analysis projects, I frequently needed to:

Collect articles for sentiment analysis
Build datasets for ML models
Analyze writing trends across topics
Archive important articles for research

Manually copying and pasting was time-consuming and error-prone. This scraper solves that problem.

🛠️ Tech Stack

Crawlee: Modern web scraping framework
Playwright: Headless browser automation
BeautifulSoup: HTML parsing
Apify SDK: Cloud infrastructure
Python 3.11: Core language

🚀 Key Features

1. Reliable Scraping

Uses residential proxies and automatic retries to avoid blocking. Handles Medium's dynamic content loading gracefully.

2. Clean Data Output

Exports structured data ready for analysis:

{
  "title": "10 Python Tips Every Developer Should Know",
  "author": "John Doe",
  "date": "Dec 15, 2024",
  "read_time": "8 min read",
  "content": "Python is a powerful programming language...",
  "subtitle": "A comprehensive guide",
  "response_count": "42"
}

3. Easy to Use

Just provide Medium article URLs and hit run. No configuration needed.

📊 Use Cases

For Researchers:

Collect articles for academic studies
Analyze content trends over time
Build corpora for NLP research

For Content Creators:

Study successful article structures
Analyze competitor content
Track writing trends in your niche

For Data Scientists:

Create training datasets
Sentiment analysis projects
Text classification models

For Marketers:

Competitive analysis
Content strategy research
Trend identification

🎓 What I Learned

Building this scraper taught me valuable lessons:

Dynamic Content Challenges: Medium loads content with React, requiring careful timing and selector strategies
Rate Limiting: Residential proxies are essential for reliable scraping
Error Handling: Robust error handling makes the difference between a toy project and production-ready tool
User Experience: Simple input schemas and clear output formats matter

🔮 Future Plans

I'm working on two companion scrapers:

Medium Comment Scraper: Extract all comments from articles
Medium Profile Scraper: Get author profiles and article lists

🚦 Getting Started

Visit apify.com/sunvic567/medium-article-scraper
Click "Try for Free"
Add your Medium article URLs
Run and download results

Pricing: Pay-as-you-go, approximately $0.10-$0.15 per 100 articles.

⚖️ Ethical Considerations

Please use responsibly:

Respect Medium's Terms of Service
Don't scrape paywalled content you don't have access to
Use for legitimate purposes (research, analysis, personal archiving)
Respect copyright - don't republish scraped content

🤝 Feedback Welcome

This is my first published Apify Actor, and I'd love your feedback! Have feature requests? Found a bug? Let me know in the comments.

🔗 Links

Try the scraper: apify.com/sunvic567/medium-article-scraper
Twitter: [@sunvic567]

What would you use a Medium scraper for? Drop your ideas in the comments! 💬

Getting into AI Agent

Sunday Victor — Tue, 16 Dec 2025 19:00:05 +0000

I started learning how to build Ai agent around November and I have built an ai question generator (it is hosted on A
pify). it takes document, extract content of the file, analyse the text extracted and select the key concept or important topic and generate question about those concepts. I would like honest feedback from more experience developer or workflow builder who will try to integrate it into their workflow what they think and what they could have done better