Long-Term Memory Systems for AI

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 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆, How I Think About It When I think about agent memory, I see it as the foundation of intelligence. An agent can only reason, plan, or act well if it remembers what it did, what it knows, and what’s relevant right now. And here’s the thing: this memory doesn’t exist magically. We feed it via context. The prompts we pass to the LLM are loaded with clues and that’s how agents simulate memory. Over time, I’ve found it helpful to break this down into five types of memory each one serving a purpose in making agents feel more intelligent, responsive, and human-like. 1. Episodic Memory This is like your personal diary, it remembers past interactions, decisions made, actions taken. Think of an agent handling a support ticket or debugging a deployment: the ability to recall prior steps matters. Most of us store this in a vector DB to capture semantic traces of these interactions. 2. Semantic Memory This is the agent’s world knowledge, everything it knows or should know. Internal docs, system behavior, org-specific rules, or fine-tuned data — all of it becomes part of the agent’s grounding. This is the backbone of retrieval-augmented reasoning. Without this, the agent’s answers float without weight. 3. Procedural Memory This one’s underrated. It holds the how, prompt templates, tool specs, available APIs, safety guardrails, system logic. It's typically tucked away in Git, prompt registries, or tool catalogs. This memory makes sure your agent knows how to execute safely and effectively. 4. Long-Term Pull (Occasional Access) Sometimes, the agent needs to fetch older knowledge, from episodic or semantic sources and temporarily load it into context to complete a task. Think of it as paging in memory that’s useful for the moment. 5. Short-Term (Working) Memory This is the glue. It pulls from all the above and forms the actual prompt we send to the LLMs/SLMs. It’s what the agent is thinking with right now. It’s context-rich, task-focused, and the most dynamic part of the whole setup. If you're building agents, think beyond the model. Think like a memory architect.

“Hello, humans. While I understand the most complex concepts of math and science – you remain a mystery,” “Your emotions, your humor and your relationship with technology require further study," ~ Aura, AI Spokesbot. When creation finds the creator to be mysterious. 'Oh no' moments get all the more louder. Relevant Must read 👇 ~ Humanizing AI Interactions. MemoryBank, a novel long-term memory mechanism designed to address the memory limitation of LLMs. "MemoryBank enhances the ability to maintain context over time, recall relevant information, and understand user personality. Besides, the memory updating mechanism of MemoryBank draws inspiration from the Ebbinghaus Forgetting Curve theory, a psychological principle that describes the nature of memory retention and forgetting over time. This design improves the anthropomorphism of AI in long-term interactions scenarios." "SiliconFriend, an LLM-based chatbot designed to serve as a long-term AI companion. Equipped with MemoryBank, SiliconFriend can establish a deeper understanding of users, offering more personalized and meaningful interactions, emphasizing the potential for MemoryBank to humanize AI interactions. The tuning of SiliconFriend with psychological dialogue data enables it to provide empathetic emotional support. Extensive experiments including both qualitative and quantitative methods validate the effectiveness of MemoryBank. The findings demonstrate that MemoryBank empowers SiliconFriend with memory recall capabilities and deepens the understanding of user behaviors. Besides, SiliconFriend can provide empathetic companionship of higher quality." Note ~ Find the link to the research paper ~ MemoryBank 'Enhancing Large Language Models with long term memory' in comments. #robotics #technology #ai Snippet : Aura from Sphere at the Venetian Resort, LasVegas. Credits ~ Mark Harrison.