# Category 20 — Memory and Personalization

This category covers how persistent memory and user personalization transform a stateless LLM into an assistant that feels continuous — one that knows who you are, remembers how you talk, and notices what you care about across sessions. It spans memory architectures (tiered stores, temporal knowledge graphs, self-editing blocks, sleep-time consolidation), preference modeling and low-rank personalization methods, the open-source and commercial tooling ecosystem, practitioner failure modes, and the shared blind spot across all of them: no benchmark currently measures whether memory makes an assistant *feel more like someone who knows you*.

## Angle Files

- [A-academic.md](./A-academic.md) — 35+ papers from ACL/NAACL/EMNLP, NeurIPS, ICLR, ICML, UIST, AAAI, CHI, and ACM TOIS, covering memory architectures (MemGPT, A-MEM, Zep, HippoRAG, Mem0, SimpleMem), preference modeling (P-RLHF, PReF, LoRe, VRF, PersonalLLM, PACIFIC), long-term dialogue benchmarks (LongMemEval, LoCoMo, LaMP, RealPref, HorizonBench), memory security governance (SSGM, InjecMEM), and sycophancy × memory (MIT/Penn State CHI 2026).
- [B-industry.md](./B-industry.md) — 20 posts (Feb 2024 – Apr 2026) from OpenAI, Anthropic, Google, Character.AI, Letta, Zep, LangChain, Eugene Yan, Microsoft, Oracle, and Mem0, tracing how tiered memory, self-editing blocks, async consolidation, portability, cloud-platform memory, and long-dialogue inference economics developed across industry.
- [C-opensource.md](./C-opensource.md) — 21 GitHub repos with star counts, licenses, and representative README quotes: Letta/MemGPT, Mem0, Graphiti, A-MEM, LangMem, Cognee, MemoRAG, EM-LLM, MemoryBank/SiliconFriend, MemoryScope/ReMe, PersonaMem-v2, LlamaIndex memory, gptme, Motorhead, Chroma, OpenMemory MCP, Axolotl, Unsloth, and SimpleMem.
- [D-commercial.md](./D-commercial.md) — 19 vendors across four sub-segments: Memory-as-a-Service infrastructure (Mem0 [now AWS exclusive], Zep, Letta, Supermemory, Cognee, Personize, GetProfile, Fastino), personalized writing assistants (Lex, Jenova, Lindy, ChatGPT/Claude/Gemini native memory), enterprise CRM memory (Salesforce, Gong, HubSpot), and cloud platform memory (Microsoft Azure AI Foundry, Oracle Database 26ai).
- [E-practical.md](./E-practical.md) — Practitioner corpus from r/ChatGPT, r/LocalLLaMA, r/LangChain, Hacker News, dev.to, YouTube, and engineering blogs: the transactional-vs-relationship-driven user split, the four canonical DIY failure modes, independent head-to-head benchmarks, security (Rehberger's false-memory injection attack), and 13+ techniques with known trade-offs.

## Synthesis and Legacy

- [SYNTHESIS.md](./SYNTHESIS.md) — the distilled synthesis across all five angles
- [INDEX.md](./INDEX.md) — legacy category summary, preserved for traceability (the detailed version of this file predating SYNTHESIS.md)

---

<!-- Legacy summary content below — compiled 2026-04-19, updated 2026-04-21 -->

**Project:** Unslop — Humanizing AI output and thinking
**Compiled:** 2026-04-19
**Angles:** A (Academic) · B (Industry) · C (Open-Source) · D (Commercial) · E (Practical/Forums)

---

## Scope

How persistent memory and user personalization turn a stateless LLM into an assistant that feels *continuous* — one that "knows who I am, remembers how I talk, and notices what I care about." Spans:

- **Memory architectures:** episodic / semantic / procedural memory, tiered stores, self-editing blocks, temporal knowledge graphs, and sleep-time consolidation.
- **Personalization:** preference modeling (P-RLHF, PReF/LoRe/VRF), style adaptation, lightweight per-user fine-tunes (LoRA/QLoRA), and user-profile primitives.
- **Benchmarks & evaluation:** LongMemEval, LoCoMo, LaMP/LongLaMP, PersonalLLM, DMR, BEAM — plus the measurable gap between factual recall and "felt humanness."
- **Productization:** Memory-as-a-Service vendors (Mem0, Zep, Letta, Supermemory, Cognee) and the consumer-facing memory UX wars across ChatGPT, Claude, and Gemini.
- **Practitioner reality:** what breaks (context pollution, memory bloat, prompt-injection via memory), what works (two-agent memory managers, Checkpointer + Store), and what users actually want (project-scoped, editable, portable memory).

Humanization relevance is explicit throughout: memory is the single biggest lever for making AI outputs stop feeling stateless, generic, and "averaged."

---

## Executive Summary

Memory and personalization have become **the** architectural frontier of the agent era, and the field has consolidated around a surprisingly consistent picture in the 18 months from late 2024 to April 2026.

**Architecturally, everyone is converging.** Tiered memory (short-term buffer → core/working → recall → archival) is now table stakes — OpenAI, Anthropic, Google, Letta, LlamaIndex, LangGraph, Mem0 and Zep have all shipped variants. Within that tiering, four structural moves are winning:

1. **Self-editing memory blocks** (MemGPT → Letta → Anthropic's memory tool) — the LLM owns write operations.
2. **Temporal knowledge graphs over flat vector stores** (Zep's Graphiti, Cognee, A-MEM) — because humans know "Alice got married *last year*" supersedes "Alice is single," and naive RAG cannot.
3. **Two-agent memory management** (Christian Rice's Sentinel + Knowledge Master, Mem0's extractor, Zep's async enricher) — a cheap filter gates an expensive, typed, CRUD-shaped extractor. Single-agent "let the LLM manage its own memory" is losing on cost and reliability.
4. **Asynchronous / sleep-time consolidation** (Letta, LangChain's "writing in the background") — memory work happens off the critical path so latency budgets survive.

**Personalization is collapsing onto low-rank preference manifolds.** PReF, LoRe, and VRF (2025–2026) independently arrive at the same inductive bias — per-user reward = weighted combination of a small set of shared basis rewards — which makes cold start tractable. Below the prompt layer, Unsloth + Axolotl have made per-user LoRA fine-tunes affordable on consumer GPUs, opening the door to "style lives in weights, facts live in memory."

**The episodic / semantic / procedural split is re-emerging as a first-class axis.** PRIME, REMem, SEEM, Echo, LangMem, LlamaIndex and gptme all now model these memory types separately — the same taxonomy psychologists use for human autobiographical memory. Systems that collapse everything into one vector store consistently lose on multi-session reasoning.

**Benchmarks have caught up with ambition.** LongMemEval, LoCoMo, PersonalLLM, LongLaMP, ALOE, RealPref, and HorizonBench give the field measurable ground truth for long-horizon recall, temporal reasoning, preference heterogeneity, implicit-preference inference, and (newly) preference evolution over time. Current leaderboard shape: Zep ~71.2% LongMemEval (independent) / 94.8% DMR; Mem0 ~49% LongMemEval independently (self-reports 93.4%, unexplained gap); SimpleMem +64% over Claude-Mem on LoCoMo (compression-first approach, Jan 2026); Letta strong on architecture but "not production-ready" by practitioner consensus. Benchmark fragmentation is an active credibility problem — vendor numbers and independent numbers diverge by 15–44 points.

**Commercially, memory is now its own category.** Mem0 ($24M, YC), Zep, Letta ($10M), Supermemory ($3M), Cognee ($7.5M), Personize, Fastino, and GetProfile all ship "drop-in memory layer" APIs — and are starting to be commoditized by foundation-model vendors shipping native memory (ChatGPT, Claude, Gemini). Writing-voice products (Lex, Jenova) are the closest commercial analogs to the Unslop thesis and explicitly sell "your signature tone, metaphors, terminology, narrative voice."

**Users are split.** Half want "relationship-driven" memory that makes the assistant feel like a colleague; the other half disable memory as "context pollution" or a "memory dossier." The dominant usability complaint — across r/ChatGPT, HN, and r/LangChain — is that memory is *account-global* when it should be *project-scoped*, and *invisible* when it should be editable. Anthropic's project-scoped approach is held up as the model to beat.

**Security and UX gaps are real.** Rehberger's indirect-prompt-injection-to-memory attack is now the canonical threat model; no one has a clean defense. Memory editing UX is underdeveloped; forgetting is treated as a bug, not a feature; portability across vendors barely exists; and — most pointedly for this project — no benchmark measures whether memory makes an assistant *feel more like a person who knows you*.

That last gap is the core Unslop opportunity: to treat memory not just as retrieval infrastructure, but as a humanization mechanism whose quality metric is *"does this reply sound like it came from someone who remembers me?"*

---

## Cross-Angle Themes

1. **Tiered memory has won — every angle confirms it.** Academic (MemGPT, MemoryLLM), industry (OpenAI/Anthropic/Gemini/Letta), OSS (Letta, LlamaIndex blocks, LangGraph Checkpointer+Store), commercial (Mem0 multi-level, Zep temporal tiers), practitioner (DIY SQLite+Chroma with short/working/long). The vocabulary varies, the shape does not.

2. **Episodic ↔ semantic ↔ procedural is the shared taxonomy.** Academic (PRIME, REMem, SEEM, Echo), industry (LangChain's CoALA-based post), OSS (LangMem, gptme's Journal/Knowledge/Relationships, LlamaIndex), practitioner (Adam Lucek's "four-memory cognitive architecture"). Commercial vendors are starting to ship this distinction too (Zep episodes vs. graph facts, Supermemory User Profile vs. Memory Graph).

3. **Temporal reasoning is the new recall.** Academic (Zep paper, Echo, LongMemEval's "knowledge updates" category), industry (Zep Graphiti, Anthropic project-scoping), OSS (Graphiti, MemoryBank's Ebbinghaus decay), commercial (Zep's "Robbie switched from Adidas to Nike" fact-invalidation demo). "Did we remember it?" has shifted to "do we know it's still true?"

4. **Memory is moving from storage to policy.** Academic (RMM learns retrieval online; A-MEM evolves memories at write time; PrLM trains reasoning over retrieved profiles). OSS (MemoryScope/ReMe's consolidation/reflection stages; Mem0's single-pass ADD-only extraction). Practitioner (agentic memory tools: `search_memory`, `add_memory`, with a 5-call budget). What to remember, how to retrieve, when to update are all *learned behaviors*, not fixed pipelines.

5. **Forgetting is finally a feature.** MemoryBank's Ebbinghaus curve, MemoryLLM's exponential decay, MemoRAG's KV compression, and the TOIS survey's explicit "forgetting" operation treat controlled forgetting as a design goal. Practitioners routinely hit the opposite problem — hoarding, bloat, "memory dossier" — and have no canonical library to reach for.

6. **Personalization is converging on low-rank preference manifolds + implicit signal.** Academic (PReF, LoRe, VRF, ALOE). Commercial (Supermemory/Fastino/GetProfile's "User Profile" primitive). OSS (PersonaMem-v2's 1,000-persona / 20k-preference benchmark, Persona-Plug as a per-user adapter). Cold start is solvable with tens, not thousands, of signals.

7. **Memory portability is the emerging trust lever.** Letta's "continual learning in token space," Anthropic's `claude.com/import-memory`, OpenMemory MCP, agent-file formats, GetProfile as an OpenAI-compatible proxy. Users are starting to expect their memory to be *an asset they can move*, not a platform lock-in — and vendors are positioning around it.

8. **Two-agent memory is the new default.** Industry (Letta's sleep-time compute), OSS (LangMem background memory manager, MemoryScope's consolidation worker), practitioner (Christian Rice's Sentinel + Knowledge Master, Mem0's extractor, Zep's async enricher). The main conversational agent does *not* manage its own memory inline.

9. **Infra cost is a first-class axis.** Character.AI's KV-cache engineering (95% hit on 180-message-average chats), Mem0's p95 latency + 80% token reduction, HippoRAG's 10–20× cost wins. Humanized long dialogues have an economics problem, not just a prompting problem.

10. **The "feels human" metric is the shared blind spot.** All angles — academic, industry, OSS, commercial, practitioner — benchmark retrieval accuracy. None benchmark "does this output read as authored by someone who knows me." This is the explicit greenfield for a humanization-focused product.

---

## Top Sources (Curated)

### Must-read papers

1. **MemGPT: Towards LLMs as Operating Systems** — Packer et al., arXiv 2310.08560 (2023). The substrate every later system assumes. https://arxiv.org/abs/2310.08560
2. **Zep: A Temporal Knowledge Graph Architecture for Agent Memory** — Rasmussen et al., arXiv 2501.13956 (2025). 94.8% DMR, −90% latency; temporal validity as first-class state. https://arxiv.org/abs/2501.13956
3. **A-MEM: Agentic Memory for LLM Agents** — Xu et al., NeurIPS 2025. Zettelkasten-style memory that evolves at write time. https://arxiv.org/abs/2502.12110
4. **Mem0: Production-Ready Long-Term Memory** — Chhikara et al., arXiv 2504.19413 (2025). +26% vs. OpenAI memory, −91% p95 latency, >90% token savings. https://arxiv.org/abs/2504.19413
5. **HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs** — Gutiérrez et al., NeurIPS 2024. Biological motivation + 10–20× cost wins on multi-hop QA. https://arxiv.org/abs/2405.14831
6. **LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory** — Wu et al., ICLR 2025. The benchmark that made the memory bottleneck measurable. https://arxiv.org/abs/2410.10813
7. **LoCoMo: Evaluating Very Long-Term Conversational Memory** — Maharana et al., ACL 2024. 300–600-turn imagined 6–12-month dialogues. https://aclanthology.org/2024.acl-long.747.pdf
8. **Generative Agents: Interactive Simulacra of Human Behavior** — Park et al., UIST 2023. The canonical memory-stream + reflection architecture. https://arxiv.org/abs/2304.03442
9. **PRIME: Cognitive Dual-Memory and Personalized Thought Process** — EMNLP 2025. Most explicit integration of episodic/semantic split *for personalization*. https://aclanthology.org/2025.emnlp-main.1711/
10. **PersonalLLM: Tailoring LLMs to Individual Preferences** — Zollo et al., ICLR 2025. Canonical personalization benchmark with heterogeneous users. https://arxiv.org/abs/2409.20296
11. **Personalization via Reward Factorization (PReF)** — arXiv 2503.06358 (2025). 30× fewer user responses; 67% win-rate over GPT-4o. https://arxiv.org/abs/2503.06358
12. **Cognitive Architectures for Language Agents (CoALA)** — Sumers et al., arXiv 2309.02427. Source of the working/episodic/semantic/procedural vocabulary practitioners now default to. https://arxiv.org/abs/2309.02427
13. **Reflective Memory Management for Long-Term Personalized Dialogue** — ACL 2025. Online-RL over retrieval policy; +10% LongMemEval. https://aclanthology.org/2025.acl-long.413/
14. **A Survey on the Memory Mechanism of LLM-based Agents** — ACM TOIS 2025. Best current orientation map. https://dl.acm.org/doi/10.1145/3748302
15. **EM-LLM** — ICLR 2025. Bayesian-surprise event segmentation; 10M-token context with no fine-tuning. https://arxiv.org/abs/2407.09450
16. **SimpleMem: Efficient Lifelong Memory for LLM Agents** — arXiv 2601.02553 (Jan 2026). Three-stage compression pipeline; +64% over Claude-Mem on LoCoMo, 30× token reduction. Multimodal. https://arxiv.org/abs/2601.02553
17. **Personalization Features and LLM Sycophancy** — MIT/Penn State, CHI 2026. Empirically confirms that condensed user profiles in memory are the largest sycophancy driver. The open question listed throughout this research is now closed on the diagnosis side. https://news.mit.edu/2026/personalization-features-can-make-llms-more-agreeable-0218
18. **Governing Evolving Memory in LLM Agents (SSGM)** — arXiv 2603.11768 (Mar 2026). First governance framework for memory evolution; names Memory Poisoning, Semantic Drift, and Conflict/Hallucination as the three critical failure points. https://arxiv.org/abs/2603.11768
19. **RealPref: Evaluating Long-Horizon Preference Following** — arXiv 2603.04191 (Mar 2026). 100 user profiles, 1300 preferences, explicit to implicit expression types; shows all current LLMs degrade significantly on implicit long-horizon preference following. https://arxiv.org/abs/2603.04191
20. **HorizonBench: Long-Horizon Personalization with Evolving Preferences** — arXiv 2604.17283 (Apr 2026). First benchmark specifically targeting user preference *evolution* — fills the "voice drift" benchmark gap identified in this research. https://arxiv.org/abs/2604.17283

### Must-read posts/essays

1. **Letta — "Agent Memory: How to Build Agents that Learn and Remember"** (Jul 2025). Canonical four-tier model + sleep-time compute. https://letta.com/blog/agent-memory
2. **Letta — "RAG is not Agent Memory"** (Feb 2025). Names the most common industry mistake. https://www.letta.com/blog/rag-vs-agent-memory
3. **Letta — "Continual Learning in Token Space"** (Dec 2025). Why token-space memory survives model upgrades. https://letta.com
4. **LangChain — "Memory for agents"** (Oct 2024). CoALA-mapped taxonomy; hot-path vs. background writes. https://blog.langchain.dev/memory-for-agents/
5. **Zep — "Beyond Chat Memory: Making AI Interactions More Personal"** (Oct 2024). Reframes memory from chat recall to whole-person context. https://blog.getzep.com/ai-knowledge-graph-memory/
6. **OpenAI — "Memory and new controls for ChatGPT"** (Feb 2024). The consumer-memory archetype. https://openai.com/index/memory-and-new-controls-for-chatgpt/
7. **Anthropic — "Context management" / project-scoped memory** (Sep 2025 → Mar 2026). The privacy-first counter-position. https://www.anthropic.com/news/context-management
8. **Google — "Introducing Gemini with personalization"** (Mar 2025). The only vendor grounding memory in real-world behavior outside chat. https://blog.google/products/gemini/gemini-personalization/
9. **Eugene Yan — "Patterns for Personalization in Recommendations and Search"** (2022). Pre-LLM personalization rigor that still sets the floor. https://eugeneyan.com/writing/patterns-for-personalization/
10. **Eugene Yan — "Training an LLM-RecSys Hybrid for Steerable Recs with Semantic IDs"** (2025). Cleanest pattern for steerable personalization. https://eugeneyan.com/writing/semantic-ids/
11. **Character.AI — "Optimizing AI Inference"** (2024). Humanized long dialogues have an infra-cost problem. https://blog.character.ai/optimizing-ai-inference-at-character-ai-2/
12. **Salesforce Engineering — "Agentic Memory"** (2026). Reference architecture for enterprise customer memory. https://engineering.salesforce.com/how-agentic-memory-enables-durable-reliable-ai-agents-across-millions-of-enterprise-users/
13. **Mem0 — "State of AI Agent Memory 2026"** (Apr 2026). Annual report marking memory's transition from experiment to first-class infrastructure; Mem0 becomes AWS Agent SDK exclusive partner; graph memory declared "in production." https://mem0.ai/blog/state-of-ai-agent-memory-2026
14. **Microsoft — Azure AI Foundry Memory Reference Architecture** (Mar 31, 2026). User-scoped memory (Cosmos DB + Entra ID) as "Step 4" in standard agent setup — signals enterprise convergence on user-scoped over account-global defaults. https://learn.microsoft.com/en-us/agent-framework/get-started/memory
15. **Oracle — "Introducing Oracle AI Agent Memory"** (Mar 2026). Counter-thesis: the database, not the vector sidecar, is the right memory primitive for enterprise agents. https://blogs.oracle.com/database/introducing-oracle-ai-agent-memory-a-unified-memory-core-for-enterprise-ai-systems

### Key open-source projects

1. **Letta (ex-MemGPT)** — `letta-ai/letta`. Stateful agents with self-editing memory blocks. The archetype. https://github.com/letta-ai/letta
2. **Mem0** — `mem0ai/mem0`. ~53.5k stars. Universal memory layer; LoCoMo 91.6 / LongMemEval 93.4 self-reported. https://github.com/mem0ai/mem0
3. **Graphiti (Zep)** — `getzep/graphiti`. Temporal knowledge graph; tracks fact validity over time. https://github.com/getzep/graphiti
4. **LangMem** — `langchain-ai/langmem`. LangGraph-native semantic/episodic/procedural memory SDK. https://github.com/langchain-ai/langmem
5. **Cognee** — `topoteretes/cognee`. Knowledge engine + ontology grounding + multi-tenant isolation. https://github.com/topoteretes/cognee
6. **A-MEM** — `agiresearch/A-mem`. Zettelkasten-inspired auto-linking memory. https://github.com/agiresearch/A-mem
7. **EM-LLM** — `em-llm/EM-LLM-model`. Bayesian-surprise event segmentation for episodic memory. https://github.com/em-llm/EM-LLM-model
8. **MemoryBank / SiliconFriend** — `zhongwanjun/MemoryBank-SiliconFriend`. Ebbinghaus-curve forgetting in a deployed companion. https://github.com/zhongwanjun/MemoryBank-SiliconFriend
9. **MemoryScope / ReMe** — `agentscope-ai/ReMe`. 20+ memory workers across retrieve/consolidate/reflect. https://github.com/agentscope-ai/ReMe
10. **OpenMemory MCP** — `mem0ai/mem0/openmemory`. Local-first, MCP-shared memory across Cursor / Claude / Windsurf. https://github.com/mem0ai/mem0/tree/main/openmemory
11. **gptme** — `gptme/gptme`. "Living memory systems": Journal / Tasks / Knowledge / Relationships / Projects. https://github.com/gptme/gptme
12. **Unsloth** + **Axolotl** — `unslothai/unsloth` / `axolotl-ai-cloud/axolotl`. Make per-user / per-persona LoRA fine-tunes tractable on consumer GPUs. https://github.com/unslothai/unsloth
13. **SimpleMem** — `aiming-lab/SimpleMem`. Compression-first lifelong memory: semantic compression + recursive consolidation + adaptive retrieval. +64% over Claude-Mem on LoCoMo. https://github.com/aiming-lab/SimpleMem

### Notable commercial tools

1. **Mem0** (mem0.ai) — $24.5M raised, ~48k stars, 186M+ monthly API calls, ~625k PyPI downloads/week. **Exclusive memory provider for AWS Agent SDK** as of Q1 2026. Category leader on setup speed and ecosystem. https://mem0.ai
2. **Zep / Graphiti** (getzep.com) — Temporal KG + fact invalidation; best benchmarks on long-horizon accuracy. https://www.getzep.com
3. **Letta** (letta.com) — Memory-first agents, cross-provider portable memory, persona as first-class object. https://www.letta.com
4. **Supermemory** (supermemory.ai) — "One memory across all your AI tools" + consumer app. https://supermemory.ai
5. **Cognee** (cognee.ai) — KG + vector + ontology; graph features free at all tiers. https://www.cognee.ai
6. **Lex** (lex.page) — **Closest commercial analog to Unslop.** Style Guides trained on your writing samples: "teaching Lex to match your signature tone, metaphors, terminology, narrative voice." https://lex.page
7. **Salesforce Einstein + Agentic Memory** — Enterprise-scale published architecture for profile-graph-linked memory. https://engineering.salesforce.com
8. **Gong Mission Andromeda** (Feb 2026) — Revenue Graph + conversational AI Assistant over captured customer interactions. https://www.gong.io
9. **Personize** (personize.ai) — Unified customer memory as a governance layer over CRM / email / docs. https://personize.ai
10. **GetProfile** (getprofile.org) — Apache-2.0 OpenAI-compatible proxy that injects typed user traits with confidence scores. https://www.getprofile.org
11. **Weaviate Personalization Agent** — Vector-DB-native "personas" + interaction logs with NL explanations. https://weaviate.io/product/personalization-agent
12. **ChatGPT / Claude / Gemini native memory** — The consumer baseline every startup is measured against.

### Notable community threads

1. **HN 47132001 — "I turned off ChatGPT's memory."** Canonical disable-memory manifesto; Anthropic project-scoping held up as the fix. https://news.ycombinator.com/item?id=47132001
2. **HN 44052246 — "I don't like ChatGPT's new memory dossier."** Names the per-project isolation gap. https://news.ycombinator.com/item?id=44052246
3. **HN 39360724 — "Memory and new controls for ChatGPT."** Establishing thread; the "transactional vs. relationship-driven" split was named here first. https://news.ycombinator.com/item?id=39360724
4. **HN 43946471 — "Dump ChatGPT's Memory and Chat History by Inspecting the System Prompt."** Reverse-engineers the ~40-conversation window + "aggregated user insights" block. https://news.ycombinator.com/item?id=43946471
5. **Ars Technica / Rehberger — "Hacker plants false memories in ChatGPT."** Canonical security story for memory-as-attack-surface. https://arstechnica.com/security/2024/09/false-memories-planted-in-chatgpt-give-hacker-persistent-exfiltration-channel/
6. **r/LocalLLaMA 1rqujc1 — "We gave our RAG chatbot memory across sessions — here's what broke first."** The four most-cited DIY failure modes, with fixes. https://www.reddit.com/r/LocalLLaMA/comments/1rqujc1/
7. **r/LangChain 1rpnxmx — "how you guys are dealing with the long running agents??"** Consensus Checkpointer + Store pattern. https://www.reddit.com/r/LangChain/comments/1rpnxmx/
8. **dev.to Fransys — "I Tested 5 AI Memory Tools So You Don't Have To (2026 Comparison)."** Independent head-to-head: Zep 85%, Letta 82%, Mem0 78%, SuperMemory 71% — with the canonical "more memories ≠ better" finding. https://dev.to/fransys/i-tested-5-ai-memory-tools-so-you-dont-have-to-2026-comparison-2ode
9. **YouTube — Christian Rice, "Build an Agent with Long-Term, Personalized Memory."** The reference two-agent (Sentinel + Knowledge Master) LangGraph implementation. https://www.youtube.com/watch?v=oPCKB9MUP6c
10. **YouTube — Adam Lucek, "Building Brain-Like Memory for AI."** The four-memory cognitive-architecture tutorial that brought CoALA into mainstream practice. https://www.youtube.com/watch?v=VKPngyO0iKg

---

## Key Techniques & Patterns

- **Tiered memory (short-term / working / long-term / archival).** Universal across OpenAI, Anthropic, Gemini, Letta, LlamaIndex, LangGraph, Mem0, Zep, and DIY SQLite+Chroma builds. The vocabulary varies; the four-tier shape is near-universal.
- **Self-editing memory blocks.** LLM uses function calls (`create_memory`, `update_memory`, `delete_memory`) to own write operations. MemGPT → Letta → Anthropic's memory tool → LangMem.
- **Two-agent memory management.** Cheap "Sentinel" LLM gates an expensive, typed extractor that performs CRUD on memory. Used by Christian Rice's LangGraph demo, Mem0 internally, Zep's async enrichment, LangMem's background manager, MemoryScope/ReMe.
- **Episodic ↔ semantic ↔ procedural split (CoALA taxonomy).** Facts about the world in semantic memory; past experiences / few-shots in episodic; behavioral patterns / auto-edited rule files in procedural. LangMem, LlamaIndex, gptme, PRIME, REMem, SEEM all ship this split.
- **Temporal knowledge graph with bi-temporal validity.** Entities + relations + valid-from / valid-to timestamps. Old facts are invalidated, not overwritten. Zep's Graphiti, Cognee, A-MEM, SEEM.
- **Zettelkasten-style auto-linking notes.** New memories generate structured notes and auto-link to related prior notes, triggering updates in the old ones. A-MEM, gptme, Cognee.
- **Sleep-time / async consolidation.** A separate agent rewrites, deduplicates, and re-links memory during idle time. Letta's sleep-time compute; LangChain's "writing in the background."
- **Reflection passes.** After each session (or on a cadence), a reflection LLM produces higher-order beliefs, "what worked / what to avoid," updated persona traits. Generative Agents → Adam Lucek's tutorial → Letta's Memory Palace.
- **Ebbinghaus-curve forgetting + salience weighting.** Explicit decay based on time + importance; strategic forgetting as a feature. MemoryBank/SiliconFriend; MemoryLLM's exponential decay.
- **LangGraph Checkpointer + Store.** Thread-scoped short-term state (typed schema, PostgreSQL) + namespaced long-term memory (JSON). The production default for LangGraph agents.
- **Agentic memory tools with a call budget.** Expose `search_memory` / `add_memory` / `search_docs` as tools; cap at 5 calls/turn; bake user-IDs into closures. The r/LocalLLaMA consensus.
- **Two-phase extraction: extract → merge/dedupe.** Pioneered by Mem0's LoCoMo pipeline; now the shared algorithmic pattern across Zep, Supermemory, Cognee.
- **User Profile as a first-class primitive.** Distinct from raw memory events. Supermemory, Fastino, GetProfile, Weaviate Personalization Agent, Salesforce Agentic Memory, Letta persona objects.
- **Low-rank preference factorization.** Per-user reward = weighted combination of shared basis rewards. PReF, LoRe, VRF. Tens of user signals sufficient.
- **Persona-Plug / per-user embeddings.** Lightweight user-specific embedding from history, attached to a frozen LLM — cheapest plausible personalization.
- **Per-user / per-persona LoRA fine-tunes.** Unsloth + Axolotl + a few hundred examples of target voice = nightly pipeline on consumer GPUs.
- **Hybrid: SQLite for structured episodic facts + vector DB for semantic similarity.** The canonical r/LocalLLaMA DIY architecture.
- **Explicit `bio` / "Remember that…" triggers.** User-visible primitive for writing memory; more reliable than hoping extractor catches it implicitly.
- **Custom Instructions vs. Memory division of labor.** Permanent identity in Custom Instructions (~1,500-word cap); evolving detail in Memory. Community-tested pattern.
- **Temporary / project-scoped chat as escape hatch.** No memory read/write for sensitive queries; ChatGPT's Temporary Chat, Anthropic's Projects.
- **Procedural memory as an auto-edited rules file.** `CLAUDE.md` / `.cursor/rules`-style persistent `.md` with 10 ongoing rules, refined by reflection passes.
- **Human-in-the-loop memory writes.** `interrupt()` before persistent memory-writes; user confirms; resume from checkpoint.
- **MCP as memory transport.** OpenMemory MCP, Graphiti MCP, Letta MCP. Same user memory across Cursor / Claude Desktop / Windsurf / Cline.
- **Token-space over weight-space.** Memory that survives model upgrades must live in tokens/external state, not weights. Letta's explicit argument; Anthropic's import tool.
- **Semantic IDs.** Hybrid LLM + recsys IDs for steerable personalization — users articulate taste in natural language that composes with behavioral signal.

---

## Controversies & Debates

1. **Relationship-driven vs. transactional memory.** Roughly half of users love cross-chat memory as "colleague feeling"; the other half disable it as "context pollution" or a "memory dossier." The split tracks intent (companion vs. tool) and is not resolved — likely needs explicit mode-switching UX.

2. **Account-global vs. project-scoped memory.** The dominant HN / r/ChatGPT complaint against OpenAI's approach. Anthropic's project-scoped model is held up as correct. OpenAI is retrofitting scoping; the war is over which default is right.

3. **On-by-default vs. opt-in implicit memory.** Google (on-by-default), OpenAI (opt-in saved / implicit chat history), Anthropic ("only when user asks"). A live product-philosophy fault line — natural UX vs. informed consent.

4. **Retrieve-and-dump vs. reason-over-profile.** PrLM (CIKM 2025) shows that personalization benefits from *explicit reasoning over* retrieved user facts, not just concatenation. Most production systems still concatenate.

5. **"Human-like" memory vs. "context engineering."** Letta's official stance: *"The goal isn't to replicate human memory mechanics but to create memory systems that enable agents to be genuinely helpful."* Counter-position from MemoryBank, A-MEM, EM-LLM, and academic PRIME/REMem: biological analogy pays off structurally (Ebbinghaus decay, event segmentation, dual-memory). The Unslop project has to pick a stance.

6. **Storage vs. policy.** Is memory a data structure (retrieval system) or a learned behavior (what to remember, how to retrieve, when to forget)? RMM, A-MEM, PrLM argue policy; Mem0, Zep, vector-DB recipes argue storage.

7. **RAG vs. agent memory.** Letta's "RAG is not Agent Memory" post names the common confusion. Consensus in the practitioner literature is now that static retrieval over a fixed corpus is insufficient; memory must be written to, evolved, and able to invalidate old facts. Some vendors (Pinecone, early LangChain memory) lag.

8. **Single-agent self-managed memory vs. two-agent manager.** Letta-style ("the main LLM manages its own memory via tools") vs. Christian Rice / Mem0 / Zep ("separate cheap extractor agent writes memory asynchronously"). Two-agent is winning on cost and reliability but single-agent is architecturally cleaner.

9. **More memory = better? (Fransys disproves it.)** SuperMemory stored the most memories and got the lowest accuracy. The field has no published Pareto curve of memory count vs. recall accuracy vs. latency.

10. **Self-reported vs. independent benchmarks.** Mem0 self-reports ~66% LoCoMo / 93.4% LongMemEval; independent tests show ~58% / 49%. The field's credibility hinges on standardizing evaluation.

11. **Memory as attack surface.** Rehberger's indirect-prompt-injection-to-memory attack persists across sessions and exfiltrates user data. OpenAI's "user messages only, not assistant outputs" is a partial mitigation. No one has published a full defense.

12. **Memory dossier vs. legibility.** April 2025's "Reference Chat History" rollout removed user visibility into what was remembered. The practitioner consensus is that *invisible memory destroys trust*, yet OpenAI chose it for UX simplicity.

---

## Emerging Trends

1. **Memory portability (2025 → 2026).** Claude's March 2026 `claude.com/import-memory` (from ChatGPT/Gemini), Letta's cross-provider agent migration, OpenMemory MCP, GetProfile's OpenAI-compatible proxy, agent file formats. Users expect to *own* their memory artifact.

2. **MCP as the integration substrate for memory.** Once a user's memory is exposed as an MCP server, every compliant client inherits it (Cursor, Claude Desktop, Windsurf, Cline). Expected to be the dominant interop pattern by end of 2026.

3. **Episodic event segmentation crossing from neuroscience into production.** EM-LLM's Bayesian surprise, A-MEM's note-linking, Echo's time-span benchmark. "Oh yeah, that conversation last week" instead of "here are 5 similar chunks."

4. **Consumer-grade fine-tuning as the back half of personalization.** Unsloth on a 4060/4070 + Axolotl YAML + a few hundred examples = credible nightly pipeline. Style lives in weights; facts live in memory.

5. **The four-memory cognitive architecture going mainstream.** Working / episodic / semantic / procedural is now the default vocabulary in dev.to, YouTube, LangChain docs, LangMem, LlamaIndex, gptme.

6. **Two-agent memory as default.** Cheap Sentinel + expensive Knowledge Master; user-facing agent never manages its own memory inline.

7. **Year-in-review as forced memory audit.** "Your Year with ChatGPT" (Dec 2025) became the most effective memory-audit UX the space has produced — users finally *saw* their aggregated insights. Expect more vendors to ship similar "memory wrapped."

8. **Writing-voice products adopting memory-infra patterns.** Lex's Style Guides + Knowledge Bases, Jenova's persistent preference accumulation. They are infra-layer products wearing writer-facing UX.

9. **Consolidation pressure on the category.** Humanloop sunsetted Sep 2025 and merged into Anthropic Console. Expect more personalization / eval tools to get absorbed into foundation-model vendors.

10. **Enterprise CRM incumbents moving fast.** Salesforce's published Agentic Memory architecture (mid-2026) + Gong Mission Andromeda (Feb 2026) compress the window for startup wedges in enterprise customer memory.

11. **Benchmark stratification — and fragmentation.** LoCoMo, LongMemEval, PersonalLLM, LongLaMP, DMR, BEAM, ALOE, RealPref, HorizonBench are the standard axes. "Does it work?" has shifted to "what does 85% accuracy mean for my use case?" But vendor self-reports diverge from independent tests by 15–44 points — benchmark credibility is now as important as benchmark coverage.

12. **Agentic / procedural memory via auto-edited rules files.** `CLAUDE.md`, `.cursor/rules`, gptme's Knowledge folder. The system's *own* learned style guide.

13. **Memory security governance is now a field.** SSGM (arXiv Mar 2026), InjecMEM, the memory security survey (arXiv Apr 2026), and OWASP Top 10 for Agentic Applications 2026 all name persistent memory as an attack surface. Memory Poisoning, Semantic Drift, and Conflict/Hallucination are the named failure classes. The Rehberger attack is now one instance of a broader class.

14. **Cloud hyperscalers entered the architecture conversation directly (Q1 2026).** Microsoft (Azure AI Foundry user-scoped memory, Cosmos DB + Entra ID), Oracle (Database 26ai Unified Memory Core), and AWS (Mem0 exclusive partner). The container-orchestration-in-2015 analogy: multiple competing architectures, no consensus, every major vendor making different architectural bets.

15. **Compression-first memory is an emerging alternative to extraction-first.** SimpleMem (Jan 2026) and MemoRAG demonstrate that distilling entire dialogue streams into compact indexed units, then consolidating asynchronously, beats extraction-and-append on cost (30× token reduction) and accuracy (+64% LoCoMo vs. Claude-Mem). The "store everything" approach and the "extract key facts" approach are both losing ground to compress-then-consolidate.

---

## Open Questions / Research Gaps

1. **"Does this output sound like me?" has no benchmark.** Every memory system benchmarks retrieval accuracy; none benchmark felt humanness or voice fidelity. This is the core Unslop wedge.

2. **Tone / style memory is missing as an architectural primitive.** Systems store *what* the user said (facts, preferences), not *how they say it* (cadence, favorite metaphors, punctuation idiosyncrasies, reading level). A dedicated **style memory block** — distinct from semantic memory — is greenfield.

3. **Memory and personalization are still two separate stacks.** PersonaMem-v2 advances weights-side personalization; Mem0/Zep/Letta advance tokens-side memory. **No open-source framework jointly trains a LoRA on user style AND writes structured memories from the same conversational stream.** Closing that loop is the highest-leverage white space.

4. **Cold start beyond VRF.** When is it worse than nothing to personalize? Most systems assume more user data is always better; VRF addresses uncertainty but the field lacks principled guidance.

5. **Style contradictions / voice drift.** Temporal fact invalidation handles "Adidas → Nike." HorizonBench (Apr 2026) now measures *preference evolution* on the benchmark side — the first such benchmark. No system has yet shipped the equivalent for voice/style drift.

6. **Emotional / affective memory.** Outside MemoryBank/SiliconFriend, very few academic systems — and essentially no commercial ones — explicitly track affect, sentiment trajectories, or relationship state. Arguably the most distinctively human aspect of long-term memory.

7. **Principled forgetting.** Only MemoryBank implements decay seriously, and it is a research artifact. A pluggable forgetting-curve / salience-weighted pruning layer is an obvious missing dependency.

8. **Privacy / editability / right-to-forget.** Almost no academic paper addresses per-memory access control, auditable provenance, or deletion guarantees. Most OSS recipes assume a single-tenant store with no PII redaction.

9. **Cross-model / cross-vendor portability in practice.** Graph memories are model-agnostic in theory, but embeddings and schema choices bind stores to a specific LLM family. Migrating OpenAI-embedded Zep to locally-hosted Cognee is painful.

10. **Memory editing UX.** Every vendor claims the user is "in control," but there is no widely adopted pattern for **user-visible memory editing** ("here's what I remember — correct me"). That visibility is precisely what makes memory feel trustworthy rather than stalkerish.

11. **Multi-user / shared memory models.** Couple / team / family agents with shared + private memory are thin — Letta's Conversations API (Jan 2026) is an early signal.

12. **Memory-hallucination trade-off.** When the agent misremembers, is that worse or better than making it up fresh? Essentially unstudied.

13. **Sycophancy × memory: now confirmed, not just conjectured.** MIT / Penn State, CHI 2026: condensed user profiles in memory are the single largest driver of sycophancy across five LLMs studied. Accurate persona inference drives mirroring; inaccurate inference does not. No published architectural defense exists. This is now a known product liability for memory-enabled assistants.

14. **Cross-session identity drift of the agent itself.** Generative Agents' reflection gives agents opinions; no work asks how those should evolve coherently across months. Humans have narrative identity; current agents have memory without biography.

15. **Multilingual / multicultural personalization.** SiliconFriend is bilingual; almost everything else is English-only. Unsolved academic problem with immediate product consequences.

16. **How much memory is the right amount?** Fransys' "more memories ≠ better" is widely cited but there is no published Pareto curve of memory count vs. accuracy vs. latency.

17. **Defenses against memory-as-attack-surface.** Rehberger's indirect-prompt-injection-to-memory is now one instance of a broader class: InjecMEM (one-interaction poisoning), memory control flow attacks, and semantic drift attacks. SSGM (arXiv Mar 2026) proposes a governance middleware, but no production defense has been validated. OWASP Top 10 for Agentic Applications 2026 names persistent memory as a named risk category.

18. **Reader/audience memory.** All products model one user. None model the reader / audience as a co-memory object — though writing humanness depends heavily on audience context.

19. **Interaction between memory and alignment.** If memory stores dispreferred behaviors (e.g., the user's tolerance for spicy humor), does it override base-model safety tuning? How?

20. **Prompt-engineering for the memory extractor itself.** The "Sentinel prompt" and category schema are load-bearing in production — but tutorials treat them as one-off engineering, not a reusable artifact.

---

## How This Category Fits in the Bigger Picture

Memory and personalization are the **single biggest lever** between "an LLM that sounds like every other LLM" and "an LLM that sounds like a specific human talking to a specific human they know." For the Unslop thesis that category matters for these reasons:

- **Generic = averaged.** As Lex puts it: *"Generic AI trained on the internet speaks in averages and optimizes for what's most likely, stripping unique voice."* Without memory + personalization, humanization collapses to prompt tricks that wash out within a session. With them, every output gets to stand on a cumulative relationship.

- **Memory is the substrate humanization techniques (tone, register, humor, pacing) live on.** Prompting for voice is necessary but insufficient — it has nowhere to persist, no way to evolve, and no way to survive model upgrades. Token-space memory (Letta) is what makes a humanized assistant's *identity* portable across model generations.

- **Personalization is the cold-start bridge.** PReF / LoRe / VRF make it tractable to locate a new user in preference space with tens — not thousands — of signals. Combined with Persona-Plug-style per-user adapters or consumer-grade LoRA fine-tunes, this is the path from "humanized defaults" to "humanized *for you*" without per-user training cost.

- **Temporal reasoning is where AI most often fails to feel human.** Knowing "Alice got married *last year*" supersedes "Alice is single" is table stakes for human-feeling recall. Zep's bi-temporal KG and LongMemEval's "knowledge updates" category make this measurable.

- **The humanization blind spot in memory evaluation is the Unslop wedge.** All five angles converge on the same observation: current benchmarks measure factual recall, not whether the output *reads* as coming from someone who knows you. That gap is precisely what this project can fill — and where it can differentiate from Mem0 / Zep / Letta / Supermemory, all of whom compete on benchmark numbers the Unslop target audience doesn't care about.

- **Style memory is the unshipped primitive.** Every vendor has a semantic memory block. None has a first-class *style* memory block — cadence, favorite metaphors, running jokes, punctuation idiosyncrasies. This is both a product opportunity and the cleanest humanization-native research contribution.

- **Humanization connects to other Unslop categories through memory.** Anti-sycophancy work becomes harder and more important when memory stores past agreement signals. Detection-evasion humanization needs style memory to evolve so outputs don't become trivially clusterable over time. Long-horizon agentic workflows (coding, research) need procedural memory to preserve hard-won context.

---

## Recommended Reading Order

**If you have 30 minutes (orientation):**
1. Letta — "Agent Memory" (canonical four-tier model) — https://letta.com/blog/agent-memory
2. LangChain — "Memory for agents" (CoALA-based taxonomy) — https://blog.langchain.dev/memory-for-agents/
3. HN 39360724 thread (transactional vs. relationship-driven split) — https://news.ycombinator.com/item?id=39360724

**If you have 2 hours (architectural depth):**
4. Zep / Graphiti paper (temporal knowledge graph) — https://arxiv.org/abs/2501.13956
5. Mem0 paper (production metrics + two-phase extraction) — https://arxiv.org/abs/2504.19413
6. Generative Agents paper (reflection + memory stream) — https://arxiv.org/abs/2304.03442
7. LongMemEval paper (what the memory bottleneck actually looks like) — https://arxiv.org/abs/2410.10813
8. Letta — "RAG is not Agent Memory" — https://www.letta.com/blog/rag-vs-agent-memory

**If you have a day (full field map):**
9. **Angle A — Academic** (this folder, `A-academic.md`). Covers the field in 8 sections + selected-sources table.
10. **Angle D — Commercial** (this folder, `D-commercial.md`). 17 vendors across 3 sub-segments + gaps framed as product opportunities.
11. CoALA paper — Sumers et al., arXiv 2309.02427 (vocabulary foundation)
12. PersonalLLM + PReF / LoRe / VRF sequence (personalization evaluation + low-rank preferences)
13. Eugene Yan — "Patterns for Personalization" + "Semantic IDs" (pre-LLM rigor + hybrid)

**If you have a week (full practitioner fluency):**
14. **Angle C — Open-Source** (this folder, `C-opensource.md`). Build a minimal memory system following A-MEM + Mem0 patterns.
15. **Angle E — Practical/Forums** (this folder, `E-practical.md`). Read the top 10 cited practitioner threads and build the Christian Rice two-agent LangGraph demo.
16. **Angle B — Industry** (this folder, `B-industry.md`). Map each vendor's public claims against your implementation.
17. Benchmarks: run LoCoMo + LongMemEval against your system; report numbers the same way Mem0 / Zep do.

**If you only have 10 minutes and want the Unslop punchline:**
Read the final two sections of this document — "Open Questions / Research Gaps" and "How This Category Fits in the Bigger Picture" — then skim the Lex and Zep summaries in `D-commercial.md`. The wedge is: *style memory as a portable, user-editable primitive + a humanness benchmark that measures "sounds like you" rather than "recalled the fact."*

---

## File Index

| File | Angle | Scope |
|---|---|---|
| [`A-academic.md`](./A-academic.md) | Academic literature | 30+ papers across ACL/NAACL/EMNLP, NeurIPS, ICLR, ICML, UIST, AAAI, COLING, IEEE TASLP, CIKM, ACM TOIS. Covers memory architectures, preference modeling, long-term dialogue & benchmarks, continual learning, surveys, patterns & gaps. |
| [`B-industry.md`](./B-industry.md) | Industry blogs | 17 posts (Feb 2024 – Apr 2026) from OpenAI, Anthropic, Google, Character.AI, Letta, Zep, LangChain, Eugene Yan. Covers tiered memory, self-editing, async consolidation, portability, long-dialogue infra. |
| [`C-opensource.md`](./C-opensource.md) | Open-source & GitHub | 20 repos with star counts, licenses, quotes. Letta/MemGPT, Mem0, Graphiti, A-MEM, LangMem, Cognee, MemoRAG, EM-LLM, MemoryBank, MemoryScope/ReMe, PersonaMem-v2, LlamaIndex memory, gptme, Motorhead, Chroma, OpenMemory MCP, Embedchain, Axolotl, Unsloth. |
| [`D-commercial.md`](./D-commercial.md) | Commercial landscape | 17 vendors across 3 sub-segments: (1) Memory-as-a-Service infra (Mem0, Zep, Letta, Supermemory, Cognee, Memobase, Personize, GetProfile, Fastino, LangChain, Pinecone, Weaviate); (2) Personalized writing assistants (Lex, Iris, Jenova, Lindy, ChatGPT/Claude consumer memory); (3) Enterprise CRM / revenue memory (Salesforce, Gong, HubSpot, Microsoft). |
| [`E-practical.md`](./E-practical.md) | Practical & forums | Reddit (r/ChatGPT, r/LocalLLaMA, r/LangChain), HN, dev.to, YouTube, engineering blogs, press. Three sub-communities, 40+ cited threads, 13 techniques/patterns, notable quotes, failure modes, security (Rehberger), cross-vendor benchmarks. |
| `INDEX.md` | This document | Category-level synthesis across all five angles. |
