读完想了想——AI 记忆工具卷了这么久，原来一直没人说清两件事根本不一样。
一种是把对话里的事实抽出来塞进向量库，问的时候捞回来。Mem0、MemPalace 这些，主打"AI 该记住什么"。
另一种是维护一堆人类能直接读写的文件，AI 在文件里干活、写回去，下一次进来上下文比上一次更厚。OpenClaw 358k star，走的就是这一路。
我在 Claude Code 上跑了快半年，选的就是后者，现在 6300 多条记忆能跨对话召回。要我说这条路是对的——每天都在复利，让我换成 Mem0 那种没有文件只有向量库的，我第一个不答应。
但真正难的是让 AI 肯读它。哪怕 P0 铁律、启动横幅、ChromaDB 向量召回、周期 audit 全上了，AI 还是会按惯性跳过记忆库直接脑补。文件结构是省事的，AI 肯不肯读才是真正的代价。
要选方案的话： 你是要 AI 记住几个事实，还是要 AI 在一摊越积越厚的上下文里干活？
前者第一种工具就够了。后者，文件只是一半——另一半是你肯花多少功夫建 harness 让 AI 真的去读。
远推 there are 450+ repos tagged "agent-memory" on github and 460+ tagged "context-management." me and my agentic best friends went through them. what I expected to find: 40 tools doing roughly the same thing with different APIs. what I actually found: two fundamentally different paradigms, almost no one drawing the line between them, and a category that doesn't have a name yet. I run a 24/7 agent setup on a Mac Mini M4. every session compounds on the last. that setup is the reason I noticed the split, most memory tools couldn't power what I'm doing, and the ones that could weren't being talked about as memory tools at all. here's the map. The Two Camps camp 1: memory backends - these tools extract facts from your conversations, store them in vector databases, and retrieve relevant ones when you ask. automated note-takers. they file things away and pull them back when needed. camp 2: context substrates - these maintain structured, human-readable context that accumulates across sessions. nothing gets "extracted." the context is the files. your agent reads them, works within them, writes back to them, and the whole thing compounds over time. camp 1 asks: "what should the AI remember?" camp 2 asks: "what context should the AI work inside?" most of the space (and most of the github stars) sit in camp 1. but camp 2 is where the architectures that actually scale to continuous, multi-session, multi-project work are emerging. and the language is starting to shift in that direction. Camp 1: The Memory Backends Mem0 — 53.1k stars the category leader by adoption. four operations: add, search, update, delete. extracts facts from conversations, stores them at three levels (user, session, agent), retrieves them via hybrid search. dead simple to integrate. python and typescript SDKs. works with everything. the limitation: memories are flat entries. no relationships between them. every extraction requires an LLM call, so quality depends entirely on how good the extraction prompt is. and once stored, they don't evolve, a fact from january sits next to a fact from april with no notion that one might supersede the other. MemPalace — 46.2k stars local-first verbatim memory. instead of extracting facts, MemPalace stores conversations word-for-word and organises them into wings (entities), rooms (topics), and drawers (original content). searches them with ChromaDB. the benchmark numbers are the highest in the space: 96.6% retrieval recall on LongMemEval using raw semantic search alone, no API calls, no LLM. 98.4% with hybrid pipeline. 99%+ with LLM reranking. the limitation: verbatim storage scales linearly. the more you talk, the bigger it gets. no compression, no synthesis. if your problem is "find the thing I said three weeks ago," this is the best tool. if your problem is "give me the current state of my work across five projects," it's the wrong tool. Supermemory — 21.8k stars positions itself explicitly as "memory is not RAG." the differentiator is temporal awareness, say "I just moved to San Francisco" and it supersedes your old city. expired facts get forgotten automatically. user profiles combine stable facts with recent activity at ~50ms retrieval. connectors for google drive, gmail, notion, onedrive, github. multi-modal across PDFs, images, videos, code. they created their own benchmark framework (MemoryBench) and claim #1 on LongMemEval, LoCoMo, and ConvoMem. most camp 1 tools treat facts as permanent. Supermemory treats them as evolving. that's the closest camp 1 gets to thinking about state, not just storage. Honcho — 2.4k stars smaller but architecturally distinct. Honcho treats both humans and agents as "peers" in a unified model. an async reasoning service runs in the background, deriving psychological insights about each peer from their sessions. it's not just remembering what you said, it's building a model of how you think. PostgreSQL + pgvector required. AGPL-3.0 (restrictive). heavier infrastructure than most. the closest thing in camp 1 to caring about entity evolution rather than just fact storage. the rest of camp 1, briefly: Cognee (15.4k) combines vector search with graph databases for relational reasoning. Memori (13.3k) intercepts LLM API calls to capture execution context, hits 81.95% on LoCoMo using only 4.97% of full-context tokens. AgentScope, MemOS, EverOS, MIRIX, SimpleMem, Memobase, all variations on the same loop. What Camp 1 Tools Have In Common every tool above runs the same fundamental loop: conversation happens → system extracts facts or stores content → facts go into a database (vector, graph, or both) → next conversation, relevant facts get retrieved and injected the intelligence is in the extraction and retrieval. the human interacts with the agent. the memory system works behind the scenes. you never touch the memory directly and you trust the system to remember the right things and surface them at the right time. this works. the benchmarks prove it. but it's solving one specific problem: fact recall. "what did I say about X?" "what does the user prefer?" there's a different problem none of these tools address. Camp 2: The Context Substrates OpenClaw — 358k stars you know what it is already, but its memory architecture is the part that matters here. plain markdown files: MEMORY.md for long-term storage, daily notes (YYYY-MM-DD.md) for running context, DREAMS.md for consolidation summaries. the line from their docs that defines the philosophy: "the model only 'remembers' what gets saved to disk, there is no hidden state." no vector database. no extraction pipeline. files the agent reads and writes to. the most interesting feature is dreaming: a background process that consolidates daily notes into long-term memory in three phases: • light sleep — screens daily notes, groups nearby lines into coherent chunks • REM — weighted recall promotion, frequently-accessed information becomes "lasting truths" • deep sleep — replay-safe promotion into MEMORY.md, reconciles rather than duplicates only entries passing all threshold gates get promoted: minimum score 0.8, minimum recall count 3, minimum unique queries 3. six weighted signals score every candidate, relevance (0.30), frequency (0.24), query diversity (0.15), recency (0.15), consolidation (0.10), conceptual richness (0.06). this is background consolidation of lived context. the system doesn't decide what's a "fact" but it promotes what keeps coming up as relevant. Zep — 4.4k stars Zep recently rebranded their entire positioning from "memory" to "context engineering." that one move is the strongest market signal in this entire landscape. a funded company with 4.4k stars looked at where the space was going and decided "memory" was the wrong word for what they were building. under the hood, Zep uses a temporal knowledge graph (their Graphiti framework). facts include valid_at and invalid_at timestamps. it extracts relationships automatically and returns pre-formatted context blocks optimised for LLM consumption. sub-200ms retrieval. SOC2 Type 2 and HIPAA compliant. Zep sits between the two camps architecturally, it still extracts and retrieves. but the rebrand is the tell. the company closest to the camp 1 / camp 2 boundary chose camp 2's language. Thoth — 145 stars tiny project, deepest architecture I found in the entire landscape. Thoth builds a personal knowledge graph with 10 entity types connected by 67 typed directional relations. FAISS vector search with one-hop graph expansion before every LLM call. the standout is the dream cycle, a nightly four-phase process: duplicate merging at 0.93+ similarity threshold → description enrichment from conversation context → relationship inference between co-occurring entities → confidence decay on relations older than 90 days three anti-contamination layers prevent cross-entity fact bleed. it's the most sophisticated automated memory refinement I found. it's sitting at 145 stars because it requires you to take the camp 2 thesis seriously enough to set up a knowledge graph for your own context. most people don't. worth watching. TrustGraph — 2.0k stars introduces "Context Cores", portable, versioned bundles that contain domain schemas, knowledge graphs, vector embeddings, evidence sources, and retrieval policies. treats context like code: version it, test it, promote it, roll it back. the framing matters. every camp 1 tool treats memory as a side effect of conversations. TrustGraph treats context as a first-class artifact with identity, versioning, and a lifecycle. you can hand a Context Core to a new agent and it inherits the full operational context. you can fork one for an experiment and merge it back. this is the closest thing in the space to what a packaged, portable unit of context looks like. the implementation is heavy (Cassandra + Qdrant), but the conceptual model is the right one. MemSearch (by Zilliz) — 1.2k stars markdown-first memory from the team behind Milvus. memories are .md files, human-readable, editable, version-controllable. Milvus runs as a "shadow index" derived from the files, fully rebuildable. the files are the source of truth. the vector search is just an access layer on top. three-layer progressive disclosure: semantic chunks → full sections → raw transcripts. hybrid search (dense vectors + BM25 + RRF reranking). what's notable is that this came from Zilliz, a vector database company. they shipped a memory system where their own product is downstream of the files. that's a meaningful concession about where the source of truth actually lives. What Camp 2 Tools Have In Common the loop is different: agent reads structured context before working → agent works within that context → agent (or background process) writes back to the structured context → next session, the context is richer than before the intelligence is in accumulation. the context is the memory. and because it's files (markdown, knowledge graphs, context containers), a human can read it, edit it, correct it, and understand exactly what the agent knows. camp 1 optimises for recall: can the system find the right fact? camp 2 optimises for compounding: does the system get better over time? Where This Is Heading, And What I'm Working On the pattern from running a 24/7 agent setup is clear. memory and context aren't the same problem. my agent doesn't need to "remember" that I prefer dark mode. it needs to operate inside a context that includes my active projects, the people I work with, recent decisions, and what happened yesterday.. and that context needs to be richer tomorrow than today. memory backends solve recall. 96%+ accuracy, sub-200ms latency, drop-in APIs. if you need a chatbot to remember user preferences, Mem0 or MemPalace will do it. but if you're running an agent continuously, one that works while you sleep, reads from the same knowledge base your other tools write to, and gets meaningfully better over weeks and months, the context substrate approach is what makes that work. my prediction is that within 6 months, "context engineering" replaces "memory" as the default term for what serious agent infrastructure does. the projects building substrate-style architectures will pull ahead of the ones still framing the problem as fact storage. the benchmarks will get rewritten or new ones will replace them. the project I'm working with is ALIVE ( alivecontext.com / @AliveContext_ ). structured context substrate, file-native, agent-agnostic. walnuts as portable context containers. zero infrastructure dependencies, plain files that compound. it's what I run on top of Hermes Agent on the Mac Mini and in Claude Code, and it's the reason that setup actually works instead of resetting every session. the category needs a name. I think it's context substrate. either way, if you're building agents that need to run for more than one conversation, you're going to end up here.