Best Practices for Memory Management in AI Conversations

Building AI agents that actually remember things 🧠 Got this excellent tutorial from Redis in my "Agents Towards Production" repo that tackles a real problem - how to give AI agents proper memory so they don't forget everything between conversations. The tutorial uses a travel agent as an example, but the memory concepts apply to any AI agent you want to build. It shows how to create agents that remember: - User preferences - Past interactions - Important context - Domain-specific knowledge Two types of memory: Short-term memory handles the current conversation, while long-term memory stores things across sessions. They use Redis for the storage layer with vector search for semantic retrieval. The travel agent example shows the agent learning someone prefers Delta airlines, remembers their wife's shellfish allergy, and can recall a family trip to Singapore from years back - but you could apply this same approach to customer service bots, coding assistants, or any other agent type. Tech stack covered: - Redis for memory storage - LangGraph (Harrison Chase) for agent workflows - RedisVL for vector search - OpenAI for the LLM Includes working code, error handling, and conversation summarization to keep context windows manageable. Part of the collection of practical guides for building production-ready AI systems. Check it out and give it a ⭐ if you find it useful: https://lnkd.in/dkjGZGiw What approaches have you found work well for agent memory? Always interested in different solutions. ♻️ Repost to let your network learn about this too! Credit to Tyler Hutcherson for creating this wonderful tutorial!

🧠 Agent Memory Isn’t Just Technical—It’s Strategic Memory isn’t just for recall. It’s the backbone of adaptive intelligence. Let’s explore how each layer turns a chatbot into a learning agent. Here's the complete memory architecture powering agents at Google, Microsoft, and top AI startups: 𝗦𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗠𝗲𝗺𝗼𝗿𝘆) → Maintains conversation context (last 5-10 turns) → Enables coherent multi-turn dialogues → Clears after session ends → Implementation: Rolling buffer/context window 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝘁𝗼𝗿𝗮𝗴𝗲) Unlike short-term memory, long-term memory persists across sessions and contains three specialized subsystems:  𝟭. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲)  → Domain expertise and factual knowledge  → Company policies, product catalogs  → Doesn't change per user interaction  → Implementation: Vector DB (Pinecone/Qdrant) + RAG  𝟮. 𝗘𝗽𝗶𝘀𝗼𝗱𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗟𝗼𝗴𝘀)  → Specific past interactions and outcomes  → "Last time user tried X, Y happened"  → Enables learning from past actions  → Implementation: Few-shot prompting + event logs  𝟯. 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗮𝗹 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗦𝗸𝗶𝗹𝗹 𝗦𝗲𝘁𝘀)  → How to execute specific workflows  → Learned task sequences and patterns  → Improves with repetition  → Implementation: Function definitions + prompt templates When processing user input, intelligent agents don't query memories in isolation: 1️⃣ Short-term provides immediate context 2️⃣ Semantic supplies relevant domain knowledge  3️⃣ Episodic recalls similar past scenarios 4️⃣ Procedural suggests proven action sequences This orchestrated approach enables agents to: - Handle complex multi-step tasks autonomously - Learn from failures without retraining - Provide contextually aware responses - Build relationships over time LangChain, LangGraph, and AutoGen all provide memory abstractions, but most developers only scratch the surface. The difference between a demo and production? Memory that actually remembers. 💬 Drop a comment: Which memory layer are you optimizing next? ♻️ Follow for breakdowns on agent architecture, memory orchestration, and system design: https://deepakkamboj.com #AIagents #MemoryArchitecture #LangGraph #LangChain #AutoGen #AgentDesign #AgenticSystems #deepakkamboj

AI doesn’t remember you. That’s a problem. Most people are surprised to learn this, but modern generative AI models—LLMs like GPT—don’t have memory. Yes, they generate responses to our questions, but the models themselves are stateless. They don’t remember past conversations. They don't even remember the last thing you asked in the current conversation. They don’t know who you are. And they have no way to retain information unless you feed it to them every. single. time. If you've played with these chatbots, you probably think I'm lying because this doesn't align with your experience. And that's true, because engineers have designed systems to store all your conversations into databases so the LLM can recall what it needs at the right. But this creates a big challenge for engineers building AI applications: How do you give AI memory? How do you make sure the right memory is recalled? How can you be sure to condense and summarize long conversations into key points into the memory without losing important information? Mem0 is an open source tool designed to solve the memory problem in LLM-based applications. Every time a user sends a message and gets a response, mem0 stores both—building a long-term memory. Mem0 does it smartly by creating a graph of the important information: what the conversation was about, what the user seems to care about, what the agent is doing, etc. It distills signal from noise, and it does it in an easy to use api where all you have to do is send mem0 the inputs and outputs, and it does the rest for you behind the scenes. Why does this matter? Because once AI has memory, we can build much better experiences. Think about: 🎓 An AI tutor that knows what you’ve already learned, so it can skip the basics and focus on what you’re struggling with. ✈️ A travel assistant that knows your past preferences. 🧪 A research agent that doesn’t keep forgetting what you’ve already told it. This isn’t needed for every AI app—but for the ones that benefit from context and personalization, a good memory tool is a game-changer.

Woah, there’s a full-blown paper on how you could build a memory OS for LLMs. Memory in AI systems has only started getting serious attention recently, mainly because people realized that LLM context lengths are limited and passing everything every time for complex tasks just doesn’t scale. This is a forward-looking paper that treats memory as a first-class citizen, almost like an operating system layer for LLMs. It’s a long and dense read, but here are some highlights: ⛳ The authors define three types of memory in AI systems: - Parametric: Knowledge baked into the model weights - Activation: Temporary, runtime memory (like KV cache) - Plaintext: External editable memory (docs, notes, examples) The idea is to orchestrate and evolve these memory types together, not treat them as isolated hacks. ⛳ MemOS introduces a unified system to manage memory: representation, organization, access, and governance. ⛳ At the heart of it is MemCube, a core abstraction that enables tracking, fusion, versioning, and migration of memory across tasks. It makes memory reusable and traceable, even across agents. The vision here isn't just "memory", it’s to let agents adapt over time, personalize responses, and coordinate memory across platforms and workflows. I definitely think memory is one of the biggest blockers to building more human-like agents. This looks super well thought out, it gives you an abstraction to actually build with. Not totally sure if the same abstractions will work across all use cases, but very excited to see more work in this direction! Link: https://lnkd.in/gtxC7kXj #generativeai #llm #machinelearning #aiarchitecture #AIModels #ArtificialIntelligence #awsAI #azureAi #GCP #MachineLearning #DeepLearning #CloudComputing #AITechnology #DataScience #Robotics #SmartSolutions #TechInnovation #AIApplications #FutureTechnology #AIResearch #NeuralNetworks #AIForGood #Automation #AICommunity #Python #TCS #AIEnhancements #AITrends #devops #NLP #MLOPS #GenAI #MultiAgentSystems #GenerativeAI #HuggingFace #Langchain #llms #EnterpriseAI #LLama #chatGPT #Analytics #DataScience #IoT #DigitalTransformation #EnterpriseAI #RAG #AgenticAI #A2A #MCP t#LangChain #FinOps #OpenAI #AutonomousAgents BrainComputerInterface #AzureAI #GCpAI #Neurotechnology #FutureOfWork #OpenSource #Innovation #AIAgents