6 Memory Optimization Techniques for Agentic Systems
...explained with code (in LangGraph)!
MongoDB’s GenAI Showcase
MongoDB’s GenAI Showcase repo is a useful resource for anyone building RAG or Agentic apps.
It’s a cookbook with several hands-on examples covering vector search, agent memory, RAG pipelines, and full-stack GenAI applications.
Includes Jupyter notebooks, Python/JS apps, self-paced workshops, and integrations with LangChain, LlamaIndex, and other frameworks.
The interesting part is that it shows how MongoDB can serve as a vector database, operational database, and memory provider in the same stack. Simplifies the infrastructure side significantly.
Thanks to MongoDB for partnering today!
6 memory optimization techniques for Agentic systems
We have added Part 16 and Part 17 to our AI Agents crash course, where we use LangGraph to implement 6 production-grade memory optimization techniques in agentic workflows.
But what exactly is Memory, and why is it so powerful for Agentic systems?
To understand this, consider an Agentic system without Memory (below):
In iteration #1, the user mentions their favorite color.
In iteration #2, the Agent knows nothing about iteration #1.
This means the Agent is mostly stateless, and it has no recall abilities.
But now consider an Agentic system built with Memory (below):
In iteration #1, the user mentions their favorite color.
In iteration #2, the Agent can recall iteration #1.
Memory matters because if a memory-less Agentic system is deployed in production, every interaction with that Agent will be a blank slate.
It doesn’t matter if the user told the Agent their name five seconds ago, it’s forgotten. If the Agent helped troubleshoot an issue in the last session, it won’t remember any of it now.
With Memory, your Agent becomes context-aware and practically applicable.
But Memory isn’t an abstract concept.
If you dive deeper, it follows a structured and intuitive architecture with several types of Memory.
Short-Term Memory
Long-Term Memory
Entity Memory
Contextual Memory, and
User Memory
Each serves a unique purpose in helping agents “remember” and utilize past information.
To simulate memory, the system has to manage context explicitly: choosing what to keep, what to discard, and what to retrieve before each new model call.
This is why memory is not a property of the model itself. It is a system design problem that can also be optimized, and we covered them in these two parts:
Also, Part 8 and Part 9 of the crash course cover memory with CrewAI:
Both parts cover:
5 types of Memory from a theoretical, practical, and intuitive perspective.
How each type of Memory helps an Agent.
How an Agent retrieves relevant details from the Memory.
The underlying mechanics of Memory and how it is stored.
How to utilize each type of Memory for Agents (implementations).
How to customize Memory settings.
How to reset the Memory if needed.
And more.
Here’s everything we did in the crash course (with implementation):
In Part 1, we covered the fundamentals of Agentic systems, understanding how AI agents act autonomously to perform tasks.
In Part 2, we extended Agent capabilities by integrating custom tools, using structured outputs, and we also built modular Crews.
In Part 3, we focused on Flows, learning about state management, flow control, and integrating a Crew into a Flow.
In Part 4, we extended these concepts into real-world multi-agent, multi-crew Flow projects.
In Part 5 and Part 6, we moved into advanced techniques that make AI agents more robust, dynamic, and adaptable, like Guardrails, Async execution, Callbacks, Human-in-the-loop, Multimodal Agents, and more.
In Part 8 and Part 9, we primarily focused on 5 types of Memory for AI agents, which help agents “remember” and utilize past information.
In Part 10, we implemented the ReAct pattern from scratch.
In Part 11, we implemented the Planning pattern from scratch.
In Part 12, we implemented the Multi-agent pattern from scratch.
In Part 13 and Part 14, we covered 10 practical steps to improve Agentic systems.
It covers everything in detail to fully equip you with the skills to build agentic systems.
Of course, if you have never worked with LLMs, that’s okay.
We cover everything in a practical and beginner-friendly way.
Thanks for reading!
Excellent breakdown of memory as a system design problem rather than model property. The distinction between short-term, long-term, entity, contextual, and user memory is so underrated, most people treat memory as a monolith when each type serves different recall patterns. What really matters is your point about what to keep, discard, and retreive, badly managed memory is worse than no memory because you end up with irrelevant context polluting every inference. Seen way too many agentic systems collapse under their own memory bagge.