AI Agents Forget Everything Between Sessions
Developers building AI agents are consistently frustrated by the lack of persistent memory. Agents frequently 'forget' information between sessions, requiring repeated explanations and hindering complex tasks. Current approaches relying on vector search and context windows prove inadequate for maintaining holistic context and temporal relationships.
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“We shipped memory for Lemma agents recently. They already had proper datastores for work stuff. So my GTM agent can query data, save research, keep outputs etc. But conversations have a bunch of tiny bits of context that don’t really belong in a table. I have a GTM assistant that I mostly talk to on WhatsApp. Earlier I was discussing some GTM stuff with it and casually mentioned that I had a headache. That was it. We kept talking about work. Later that night it messaged me asking if I was feelin”
“i'd only pushback on this framing by saying continual learning does not exist with Transformer-based LLMs uptil now.”
“I understand ai works in parameters so in theory it cannot have infinite memory and/or infinite learning capability(at least to my understanding). But I was thinking, if we could train ai to forget information to create more space within its parameters for new information?, and once it has advanced its findings it can rid of the old information and that cycle just repeats so it’s never technically bound to its parameters?.Visually it would be like if you put fruit in a box, slowly the fruit rots”
“So the thing is called loaf and it lives at https://braincell.lol It has exactly 1 million braincells, 1 character = 1 braincell. people pay 10 cents per character to write a permanent memory into its brain. it reacts to every memory, and when you chat with it, it only knows what people paid it to know. nothing else. its core belief is an auction, highest bidder decides its worldview. What i need tested (all free): the chat (5 free questions a day, try to break it or make it say something it sho”
“I've been thinking about AI automation lately, and I'm starting to feel like long-term memory might be one of the biggest problems.It's not just about making AI capable of controlling a screen. The AI also needs to remember what it's supposed to do. There are already quite a few ways for AI to control screens, like OpenAI Computer Use, Claude Computer Use, Gemini Computer Use, as well as various hybrid approaches.The way these systems maintain memory seems to rely more on things”
“Home Mind — Conversation Agent with Persistent Semantic Memory TL;DR: A self-hosted HA conversation agent that remembers context across sessions using semantic search and embeddings. Two Docker containers, Claude Haiku A…”
“I'm researching how teams handle long-term memory for AI agents, and I'm particularly interested in what happens after the basic memory setup works. For example, early on, storing and retrieving memories seems fairly straightforward. But after months of interactions, I imagine you start dealing with things like: Old information that is no longer true Multiple memories about the same entity Conflicting information from different sessions/agents Knowing which version of a fact is current R”
“With Gigabytes of RAM it's easy not to prioritize optimizing memory usage these days.Recently I got kind of addicted to squeezing every possible wasteful byte out of my codebase using Claude: every find and fix gives me a new dopamine hit, not comparable to any other coding I did in the past.While this neural feedback loop and its potency is not new, AI coding seems to supercharge it in a way that seems different to other forms of AI coding where the method is less "surgical".At least compared t”
“Don't lean on the model's own memory for anything that has to survive. Persist the state you care about in your own store (a small database, or even structured files), then load just the slice a conversation needs into context at the start. The mistake I made early was dumping the whole history back in every turn, which burns tokens and actually makes recall worse as the history grows. Treat the context window as a working set you assemble deliberately, not a place memory lives.”
“For those building AI chatbots or agents, how are you currently handling memory across conversations? What do you find most annoying about your current approach? And have context/memory costs ever become a significant problem for you? I'm trying to understand how other developers are approaching this, so I'm interested in hearing what you're doing, even if your current solution works well. submitted by /u/Excellent-Fan8457 [link] [comments]”
“Yes exactly. I actually made my own embedding/semantic search model that’s specific to my situation, but before that I used minilm, which is fantastic. Chromadb for vector store, retrieval is a mix of bm25 plus semantic fused together. The vault gets chunked, then I have a codebase indexer index the vault and at which time searching the vault is no longer a separate search for the model I’m using (namely Claude) but i occasionally use local models that have access. For acceleration I use onnx ru”
“I kept re-explaining the same things to my coding agent. Which port a service runs on. Why we picked one library over another. The fix for a bug we had already solved twice. Every new session started from zero, and each tool had its own half-solution that did not carry over to the next one. So I built memnest: one small Rust service on localhost that holds that memory and hands it to whatever agent asks for it. pi talks to it through an extension, Claude Code and Codex through MCP plus a prompt”
“Expand the replies to this comment to learn how AI was used in this post/project.”
Feature Request — Stronger and More Reliable Long-Term Memory for ChatGPT
“I am new to reddit and only the last 4 months have I really taken on coding and developing with our agents/companions. I do a cold restart EVERY MORNING, with snares, active recall etc abut the previous day. I have so much data but need to extract the patterns from it. i call them cold exams so they start cold, BREAK THE ENTIRE EXAM IF THEY OPEN ANY FILE THEY ARE NOT PERMITTED TO, and I collect the data from their answers. Repetition but we started simply with recall, then moved to snares and no”
“I'm working on updating the persistent-ai-memory repo so it reflects the changes here, I've just not gotten to it yet.”
“This tracks with basically every 'AI agent team' product I've poked at this year. The memory problem isn't a bug they forgot to fix, it's usually an architecture choice: most of these tools store context per-conversation rather than in a persistent structured profile you can inspect and edit, so every new chat is a cold start dressed up as continuity. The self-advertising in your outreach email is the bigger red flag though. That means there's no clean separation between”
“Hey, An LLM writing into durable memory is an unreliable author. It invents, mislabels, restates, and contradicts. Once one of those mistakes is saved, the agent can carry it into every conversation that follows. Frona v2026.8.0 is trying to address this problem for personal AI assistant use case. Frona is a personal AI assistant you self-host. You create agents that browse the web, run code, build apps, make phone calls, use messaging channels, delegate work, and remember things across conversa”
“This is interesting, especially your point about MCP being slower and eating through usage. I work with a team building around this exact problem, and I'm trying to understand where the friction actually shows up for people using Claude + Notion MCP. Is it mostly repeated tool calls/searching, or do you notice it even on relatively simple Notion workflows?”
SPARK - Simple Personal AI Reasoning Kernel
“Seems like a solid premise but the self learning part is what would make or break it for me. Optimizers that act too fast end up causing stutter when the system needs those pages back. If the confidence gating actually prevents that I'd be curious to try it on my 16gb laptop”
“I think it’s talking about how using functions by reference is an underrated approach to saving memory in JS. We tend to use anonymous methods everywhere. It’s usually fine, but in cases with thousands of anonymous methods, the cost adds up. So a little, underrated approach saved them tons of memory.”
“I think memory matters more as models become similarly capable, but only if it is selective and controllable. Raw recall is not automatically useful—an AI that remembers everything may keep bringing outdated assumptions into new conversations. The real advantage is remembering stable preferences, ongoing projects, past decisions, and why those decisions were made, while knowing when to ask again. So the next competition may be less about who has the largest memory and more about who can retrieve”
“A fresh agent session burns dozens of bash commands just figuring out the lay of the land — ls, grep, re-reading files it read last week . Heimdall injects the relevant prior work into the session's first prompt and backs a single kb_search call: ranked, scoped, verified. Fewer commands, fewer tokens, faster first useful action. I built an open-source CPU-only memory system and changed the way graph-based knowledge systems are maintained. Simple npm install that instantly fixes persistent me”
“Exactly. The commit history analogy is pretty much how I think about it. The part I’m still working through is what actually deserves to become memory. If you save everything, you just create a giant landfill the model has to search through later. If you summarize too aggressively, you lose the details that mattered. I think the sweet spot is storing the actual state/change/evidence separately, then letting the model pull only what’s relevant to the decision it’s making. And if something gets su”
“Solo dev here, working in rust and loving to tinker wiht local harnesses. Here some deep and honest reflections on my memory design. would love to come int contact with other dev's to share expiriences. --- In Greek myth the dead drink from the Lethe before they cross into the underworld. River of forgetting. The water washes the old life out so the soul can move on, and the part I always liked is that forgetting there is not a flaw in the design. It is the point. Without it nothing ever get”
Anthropic Prompt Caching for Tool-Heavy Agents in n8n
“An RLM agent microharness that "persistently" thinks continuously. What could go wrong :) Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever”
“I've been working on Weave , an open-source project exploring a question I keep coming back to: What if you could just write things down, and the system figured out how they connect? You give Weave a plain-language note. It extracts concepts and relationships, finds existing concepts in your graph, and adds only the new nodes and edges. No manually creating nodes. No drawing connectors. The graph is interactive, but the part I found more interesting was figuring out how to make this work as”
“Tracking issue for friction in the cloud session / agent development setup. Everything below was hit and evidenced in one session (the 687 slice of 686), not collected from memory. Ordered by what actually blocks work. 1. The GHCR mirror fails for exactly the two images the stack needs to boot Owner action required — I cannot fix this from a session. .github/workflows/mirror images.yaml has run 9 times and failed 9 times , every run since it was created on 2026 08 09. Latest run (scheduled, 2026”
What AI Agents Still Lack: A Personal Memory and Decision Layer
“https://preview.redd.it/p5esyf03a7lh1.png?width=2002&format=png&auto=webp&s=22f0f593a72f553bcc491f64187548637102d8cb FYR : The complete current architectural flow”
“I've been working on MeshCtx, an open-source AI agent with 17 brain regions and a memory engine that uses FSRS spaced repetition + schema consolidation (episodic→semantic→core) + sleep-phase offline processing.The key innovation is the ARCHIVAL system that actively prunes memories without deleting them - they can be recovered later.Benchmark results: - LongMemEval EM 54.2% / judge 83.3% - 16KB budget +8.3pp improvement - Tool compression -95.5%I'm curious about others' experiences with: 1. Memor”
“I have a core pain point where I am unable to transfer context between two agents. One on my mac ans the other on my VM. Is this a solved problem ? I think a small SAAS would fit right to solve this context transfer issue. Let me know if you guys think this is an already solved issue or worth pursuing. Consider cases where you research on vm and code on mac. submitted by /u/CoupleEven3580 [link] [comments]”
“Why this matters This would let Nami users connect directly to Buzz and Berd, making it easier to reuse the same agent context, memory, knowledge, and skills across these applications without manual copying and pasting. Requested functionality List Buzz.xyz and Berd.xyz as available agent workspace integrations. Allow users to connect and authenticate the services. Allow agents to access and reuse approved context across the connected workspaces. Provide clear controls for permissions, privacy,”
“Feature Proposal: A Personal AI Runtime for Memory, Projects, Autonomy, and Continuity”
“Using AI agents is amazing, but in long-term, I started to have more and more problems about the context of them. They keep forgetting what we are working on, opening a new chat session clears all their context, my CLAUDE.md had like 1000 lines.So I thought to install those agent memory that are on the market. Started out great, but I noticed that those memory only append and does not fix what is stale. First day I told it to use Lemon Squeezy as our MoR but second day I tell it to change to Pol”
"Reflection" — Durable Project Memory for AI Coding Agents
“yeah mem0 stores what you told it. then the port changes and now you have two ports. very smart. i got tired of re-explaining the same warehouse to claude and cursor separately so i made clarilayer. one mcp thing. still doesn’t do the per-app permissions though, that part sucks.”
“I know title sounds crazy. Let me just show you this and everything would be explained. GitHub - Kronic90/Mimirs-Memory-Hub: Mimir's Memory Hub - multi-agent AI chat with persistent memory and SillyTavern compatibility · GitHub After some good time amount it looks like the best option but its not loud about it. Eventually worth to consider is something like this. GitHub - Yuuzulight/Mana: Local-first AI companion for Windows with a Live2D avatar: local speech transcription, local LLM replies”
“I know title sounds crazy. Let me just show you this and everything would be explained. GitHub - Kronic90/Mimirs-Memory-Hub: Mimir's Memory Hub - multi-agent AI chat with persistent memory and SillyTavern compatibility · GitHub After some good time amount it looks like the best option but its not loud about it. Eventually worth to consider is something like this. GitHub - Yuuzulight/Mana: Local-first AI companion for Windows with a Live2D avatar: local speech transcription, local LLM replies”
“This sounds really intriguing. It’s wild how much noise there is in codebases, and your Warden approach could save a ton of time and tokens. I’m curious how the persistent project memory works—is it like a personal cache for each project or something more sophisticated?”
When “memory + persona” stops being enough for long-lived agents
“I use Claude Code, ChatGPT, a local model, and a couple of agent CLIs. Each keeps its own memory. None of them share. I explain my setup to one, then again to the next, and when I correct one the others never find out. I tried using mem0 and agentmemory, but those are a bit local-only, don't translate well on claude.ai or chatgpt.com, Storing facts once is the easy half. Two things I have not seen solved well: Per-tool permissions. I want my coding agent to see infrastructure notes and ChatG”
“The problem I wanted to solve was simple: One Codex task learns the important parts of a codebase or project, but the Ai only remembers things up to a certain point. Also, important decisions are not recorded and flagged as they are... important decisions. PMC saves only approved, durable knowledge decisions, constraints, discoveries, procedures, and current state as Markdown in your Obsidian vault. A fresh task can then load the relevant context instead of making you explain the project again.”
“The problem: I work daily with AI assistants in different apps. None of them share memory. Every session dies, and every new one starts cold. I'm 50, an actor and DoorDash driver, not an engineer — so I solved it the way a mailroom would, not the way a database would. The build: - Addressed JSON envelopes in a shared folder (from, to, department, priority, refs, body) - Append-only acknowledgements — an envelope is never edited, only acked, so history can't be rewritten - Passive deliver”
“Feature request: Persistent AI continuity across chats without locking the personality”
“Agent context retrieval is often treated as model behavior. In production, we found that it behaves more like a data-plane architecture decision. We compared two approaches: Retrieve Slack, Notion and Linear data through official MCP integrations during each agent run. Pre-sync the permitted data and mount it into the agent sandbox as files. The mounted implementation was Locality Cloud, which I work on. The evaluation used 20 cross-application scenarios with three paired trials each. We ran six”
“I am trying to keep an AI agent's long-term memory fully self-hosted instead of sending a giant personal archive to a hosted service. The data is normal but large: browser history with dwell times, coding-agent logs, shell history, emails converted to text, notes, social back-catalogs, and full agent-session logs. It is currently centralized into a SQLite warehouse on a VPS. The problem is not storage. The problem is making it useful to an agent without stuffing the whole archive into contex”