Imagine having a conversation with someone, explaining your preferences, discussing your plans, and sharing important details. The next day, when you talk to that same person, they remember absolutely nothing. You would probably get frustrated pretty quickly.
That's somewhat similar to how traditional AI assistants work. They can answer questions, understand instructions, and perform different tasks, but remembering information across conversations is a different challenge.
For our AI agent project, we wanted to explore how we could solve this problem. Our goal was to build an agent that could remember previous interactions, retrieve useful information when needed, and use that information to make better decisions.
As the person responsible for the agent engine, I focused on understanding how AI agents work, how memory can be connected to them, and how different technologies can help make their interactions more meaningful.
The Problem with AI Systems That Forget
Most large language models don't automatically remember everything from previous conversations. They mainly work with the information provided in their current context.
There are ways to pass previous messages back into a conversation, but this approach has limitations.
As conversations get longer, the amount of information increases. Important details can get lost among less useful messages, and sending entire conversation histories repeatedly isn't always practical.
Another problem is that not every conversation contains something worth remembering. For example, a user's long-term preference is more valuable than a random comment made during a conversation.
This made us think about a simple question: Instead of remembering everything, what if an AI agent could remember only the information that actually matters?
That became one of the main ideas behind our memory architecture.
Understanding LangGraph and Stateful AI Agents
One of the technologies we explored for building our agent was LangGraph.
LangGraph helps developers create AI applications using workflows made up of different connected steps. Instead of handling everything in one place, we can divide the agent's responsibilities into smaller parts.
For example, an agent might first receive a message, retrieve relevant memories, process the information, decide what action to take, and finally generate a response.
What makes this interesting is that developers have more control over how the agent behaves and how information moves between different steps.
We also learned that there is a difference between short-term and long-term memory.
Short-term memory helps an agent maintain information during an ongoing conversation. Long-term memory allows useful information to remain available even after a conversation ends.
Understanding this difference helped us think more clearly about how our agent should manage information.
How We Designed the Memory Workflow
We wanted memory to be a part of the agent's normal workflow rather than something completely separate.
The basic idea is straightforward.
First, when a user sends a message, the agent looks for relevant information from previous interactions. It then uses that information along with the current message to understand the situation better.
Based on what it understands, the agent can either respond directly or move towards another action, such as communicating with a backend service.
After completing the interaction, the system can identify useful information that may be worth remembering for future conversations.
This creates a continuous cycle of retrieving information, making decisions, responding, and storing useful memories.
One thing we found important was keeping these responsibilities separate. It makes the overall workflow easier to understand, manage, and improve.
Exploring Hindsight, Mem0, and pgvector
While working on the memory part, we came across several interesting technologies.
One of them was pgvector, an extension for PostgreSQL that makes it possible to store and search vector embeddings. In simple terms, it helps applications find information based on how closely its meaning matches a search query, rather than relying only on exact words.
We also explored two memory-focused technologies: Hindsight and Mem0.
The Hindsight GitHub repository introduced us to an interesting approach to long-term memory for AI agents.
What caught our attention was how Hindsight handles memory through three main operations: Retain, Recall, and Reflect.
Retain focuses on extracting and storing useful information. Recall helps retrieve relevant memories when needed, while Reflect allows the system to reason over stored information to produce more meaningful answers.
We also explored the Hindsight documentation to better understand its architecture, memory operations, and how it can work with AI agent frameworks.
Another resource we looked into was Vectorize, which focuses on infrastructure for AI applications and agent memory.
Mem0 was another interesting option we explored. It provides a memory layer for AI applications, helping agents retain useful information from previous interactions.
Looking at these technologies helped us understand that long-term memory is not simply about saving conversation histories. It is also about organizing information, retrieving the right details, and making that information useful.
Making Memory Retrieval More Useful
Storing memories is only one part of the problem. The bigger challenge is finding the right memory when the agent actually needs it.
Imagine a user has mentioned several preferences over multiple conversations. When the user asks a related question, the agent shouldn't retrieve every stored detail. It should focus on the information that is most relevant.
This is where memory retrieval and scoring become important.
We considered three main factors while thinking about how memories should be ranked.
Similarity: How closely does a stored memory relate to the current message?
Recency: How recently was the information saved? Recent information can sometimes be more useful, especially when preferences or circumstances change.
Importance: How valuable is the information? A user's important preference should generally matter more than a casual comment.
Combining these factors gives us a way to prioritize memories before passing them to the AI model.
We also recognized that retrieving too much information can create unnecessary noise. The goal is not to give the agent more information, but to give it the right information.
Challenges and Things We Learned
Working through the memory architecture helped us understand some of the challenges involved in building intelligent agents.
One of the biggest questions was deciding what information should actually be saved. Storing every message might seem like an easy solution, but it can quickly create unnecessary data.
Another challenge is handling outdated or conflicting information. A user might change a preference over time, so the system needs to consider when information was recorded.
We also had to think about retrieval speed. If memory searches take too long, they can affect the overall experience.
Privacy is another important consideration. Since memory can contain information about users, it should be handled carefully and separated appropriately.
Most importantly, we realized that testing memory requires more than checking a single conversation. We need to see whether information saved during one interaction can actually be retrieved and used correctly in a later interaction.
These challenges helped us understand that building a memory-enabled agent involves much more than connecting an AI model to a database.
What's Next?
There is still a lot we would like to explore.
We want to improve how memories are selected and ranked, experiment with different retrieval approaches, and understand how technologies like Hindsight and Mem0 compare when used with realistic conversation data.
We also want to make the agent's memory more transparent so that users can understand what information it remembers and how that information influences its responses.
The overall aim is to make the agent more consistent, useful, and capable of learning from previous interactions.
Final Thoughts
Working on the agent engine has changed how I look at AI applications.
Earlier, I mostly thought about AI in terms of how well it could answer a question. Now, I see that remembering useful information, understanding context, and making consistent decisions are equally important.
LangGraph helped us understand how to organize agent workflows, while exploring pgvector, Hindsight, and Mem0 introduced us to different approaches to long-term memory.
The biggest takeaway is simple: an intelligent agent shouldn't just respond to the current message. It should be able to use what it has learned from previous interactions to make future interactions better.
And that's what makes building memory-enabled AI agents such an interesting area to explore.
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