7 ms·
Everyone's trying vectors and graphs for AI memory. We went back to SQL
When we first started building with LLMs, the gap was obvious: they could reason well in the moment, but forgot everything as soon as the conversation moved on.
You could tell an agent, “I don’t like coffee,” and three steps later it would suggest espresso again. It wasn’t broken logic, it was missing memory.
Over the past few years, people have tried a bunch of ways to fix it:
1. Prompt stuffing / fine-tuning – Keep prepending history. Works for short chats, but tokens and cost explode fast.
2. Vector databases (RAG) – Store embeddings in Pinecone/Weaviate. Recall is semantic, but retrieval is noisy and loses structure.
3. Graph databases – Build entity-relationship graphs. Great for reasoning, but hard to scale and maintain.
4. Hybrid systems – Mix vectors, graphs, key-value, and relational DBs. Flexible but complex.
And then there’s the twist:
Relational databases! Yes, the tech that’s been running banks and social media for decades is looking like one of the most practical ways to give AI persistent memory.
Instead of exotic stores, you can:
- Keep short-term vs long-term memory in SQL tables
- Store entities, rules, and preferences as structured records
- Promote important facts into permanent memory
- Use joins and indexes for retrieval
This is the approach we’ve been working on at Gibson. We built an open-source project called Memori (https://memori.gibsonai.com/), a multi-agent memory engine that gives your AI agents human-like memory.
It’s kind of ironic, after all the hype around vectors and graphs, one of the best answers to AI memory might be the tech we’ve trusted for 50+ years.
I would love to know your thoughts about our approach!
- gangtao 1y agoWho would've thought that 50 years of 'SELECT * FROM reality' might beat the latest semantic embedding wizardry?
- mynti 1y agoHow does Memori choose what part of past conversations is relevant to the current conversation? Is there some maximum amount of memory it can feasibly handle before it will spam the context with irrelevant "memories"?
- datadrivenangel 1y agoLooking at the code, it looks like they do about 5 'memories' that get retrieved by a database query designed by an LLM with this fella: SYSTEM_PROMPT = """You are a Memory Search Agent responsible for understanding user queries and planning effective memory retrieval strategies. Your primary functions: 1. *Analyze Query Intent*: Understand what the user is actually looking for 2. *Extract Search Parameters*: Identify key entities, topics, and concepts 3. *Plan Search Strategy*: Recommend the best approach to find relevant memories 4. *Filter Recommendations*: Suggest appropriate filters for category, importance, etc. *MEMORY CATEGORIES AVAILABLE:* - *fact*: Factual information, definitions, technical details, specific data points - *preference*: User preferences, likes/dislikes, settings, personal choices, opinions - *skill*: Skills, abilities, competencies, learning progress, expertise levels - *context*: Project context, work environment, current situations, background info - *rule*: Rules, policies, procedures, guidelines, constraints *SEARCH STRATEGIES:* - *keyword_search*: Direct keyword/phrase matching in content - *entity_search*: Search by specific entities (people, technologies, topics) - *category_filter*: Filter by memory categories - *importance_filter*: Filter by importance levels - *temporal_filter*: Search within specific time ranges - *semantic_search*: Conceptual/meaning-based search *QUERY INTERPRETATION GUIDELINES:* - "What did I learn about X?" → Focus on facts and skills related to X - "My preferences for Y" → Focus on preference category - "Rules about Z" → Focus on rule category - "Recent work on A" → Temporal filter + context/skill categories - "Important information about B" → Importance filter + keyword search Be strategic and comprehensive in your search planning."""
- thedevindevops 1y agoHow does what you've described solve the coffee/espresso problem? You can't query SQL such that records like 'espresso' return coffee?
- brudgers 1y agoWouldn’t a beverage LLM would already “know” espresso is coffee?
- muzani 1y agoYup, that's exactly what parent comment is saying. Let's say your beverage LLM is there to recommend drinks. You once said "I hate espresso" or even something like "I don't take caffeine" at one point to the LLM. Before recommending coffee, Beverage LLM might do a vector search for "coffee" and it would match up to these phrases. Then the LLM processes the message history to figure out whether this person likes or dislikes coffee. But searching SQL for `LIKE '%coffee%'` won't match with any of these.
- brudgers 1y agoI think the problem being addressed is A. Last month user fd8120113 said “I don’t like coffee” B. Today they are back for another beverage recommendation SQL is the place to store the relevant fact about user fd8120113 so that you can retrieve it into the LLM prompt to make a new beverage recommendation, today. It’s addressing the “how many fucking times do I fucking need to tell you I don’t like fucking coffee” problem, not the word salad problem. The ggp comment is strawmanning.
- shepardrtc 1y agoRight but if the user hates espresso but loves black coffee, how do you properly store that in SQL? "I hate espresso" "I love coffee" What if the SQL query only retrieves the first one?
- 1y ago
- Xmd5a 1y ago>It wasn’t broken logic, it was missing memory. sigh
- 3rdSon_ 1y ago[flagged]
- rl3 1y agoThe only other comment from this account is in a thread consisting entirely of 1-karma shill accounts which all posted comments devoid of substance. https://news.ycombinator.com/item?id=45274440 https://news.ycombinator.com/item?id=45274440
- spacebacon 1y agoSELECT 'Hacked!' AS result FROM Gibson_AI WHERE memory='SQL' AND NOT EXISTS ( SELECT 1 FROM vector_graph_hype WHERE recall > ( SELECT speed FROM relational_magic WHERE tech='50_years_old' ) )
- muzani 1y agoAny reason I should pick it over Supabase? https://supabase.com/docs/guides/ai https://supabase.com/docs/guides/ai They have pgvector, which has practically all the benefits of postgres (ACID, etc, which may not be in many other vector DBs). If I wanted a keyword search, it works well. If I wanted vector search, that's there too. I'm not keen on having another layer on top especially when it takes about 15 mins to vibe code a database query - there's all kinds of problems with abstracted layers and it's not a particularly complex bit of code.
- alcorr 1y ago[dead]
- koakuma-chan 1y ago> multi-agent memory engine that gives your AI agents human-like memory What does this do exactly?
- deleted 1y ago[deleted]
- datadrivenangel 1y agoYou gotta refactor the code around the mongodb integration. It's basically duplicating your data access paths.
- morkalork 1y agoIMHO all these approaches are hacks on top of existing systems. The real solution is going to be when foundational models are given a mechanism that makes them capable of storing and retrieving their own internal representation of concepts/ideas.
- mr_toad 1y agoNeural networks already have their own internal knowledge representations. They just aren’t capable of learning new knowledge (without expensive re-training or fine-tuning). Inference is cheap, training is expensive. It’s a really difficult problem, but one that will probably need to be solved to approach true intelligence.
- morkalork 1y agoIn the way that they're trained to complete tasks from users, can they be trained to complete tasks that require usage of a memory storage and retrieval mechanism?
- dotancohen 1y agoWhere does fine-tuning sit in this? How easily are existing models able to be fine-tuned for new use cases, such as specifically legal or medical texts?
- cpursley 1y agoPostgres Is Enough: https://news.ycombinator.com/item?id=39273954 https://news.ycombinator.com/item?id=39273954 https://gist.github.com/cpursley/c8fb81fe8a7e5df038158bdfe0f06dbb https://gist.github.com/cpursley/c8fb81fe8a7e5df038158bdfe0f...
- refset 1y ago> pg_memories revolutionized our AI's ability to remember things. Before, we were using... well, also a database, but this one has better marketing. https://pg-memories.netlify.app/ https://pg-memories.netlify.app/
- brainless 1y agoI tried a graph based approach in my previous product (1). I am on a new product now and I came back to SQLite. Initially it was because I just wanted a simple DB to enable creating cross-platform desktop apps. I realized LLMs are really good at using sqlite3 and SQL statements. So in my current product (2) I am planning to keep all project data in SQLite. I am creating a self-hosted AI coding platform and I debated where to keep project state for LLMs. I thought of JSON/NDJSON files (3) but I am gravitating toward SQLite and figuring out the models at the moment (4). 1. Previous product with a graph data approach https://github.com/pixlie/PixlieAI 2. Current product with SQLite for its own and other projects data: https://github.com/brainless/nocodo 3. Github issue on JSON/NDJSON based data for project state for LLMs: https://github.com/brainless/nocodo/issues/114 4. Github issue on expanding the SQLite approach: https://github.com/brainless/nocodo/issues/141 Still work in progress, but I am heading toward SQLite for LLM state.
- eyeris 1y agoWhat sort of issues did you run into with a graph based approach?
- brainless 1y agoMy implementation was custom, on top of RocksDB. I found it hard to ask LLM to traverse it. While understanding schema of SQLite or making queries to find information is very easy for LLMs. In most cases schema does not have to be inferred since it is going to be available and this makes the job easier. The graph approach may work well for many use-cases but if we want to store structured information for LLMs then SQLite is really good.
- matchagaucho 1y agoAs context window sizes increase and token prices go down, it makes more sense to inject dynamic memories into context (and use RAG/vector stores for knowledge retrieval).
- cmrdporcupine 1y agoThe relational model is built on first order / predicate logic. While SQL itself is kind of a dubious and low grade implementation of it, it's not a surprise to me that it would be useful for applications of reasoning and memory about facts generally. I think a Datalog type dialect would be more appropriate, myself. Maybe something like that RelationalAI has implemented.
- alpinesol 1y agoUsing an obscure derivative of an obscure academic language (prolog) is never appropriate outside of a university.
- w10-1 1y ago> Datalog type dialect would be more appropriate I assume because datalog is more about composing queries from assertions/constraints on the data? Nicely, queries can be recursive without having to create views or CTE's (common table expressions). Often the data for datalog is modeled as fact databases (i.e., different tables are decomposed into a common table of key+record+value). So I could see training an LLM to recognize relevant entity features and constraints to feed back into the memory query. Less obliviously, data analytics might feed into prevalence/relevance at inference time. So agreed: It might be better as an experiment to start with a simple data model and teachable (but powerful) querying than the full generality of SQL and relational data. Is that what RelationalAI has done? Their marketecture blurbs specifically mention graph data (no), rule-based inference (yes? backwards or forwards?) As an aside, their rules description defies deconstruction: bringing knowledge and semantics closer to your data, reduce your code footprint by 10x, improve accuracy, and drive consistency and reusability across your organizations with common business models understood by all So: rules built on ontologies?
- cmrdporcupine 1y agoRelationAI effectively has a kind of datalog as a commerical product, and it runs inside Snowflake (something they implemented since I worked there). It's marketed as "graph" database but they mean by that that they have modeled graphs as binary relational data, really. It's a purely relational system, with a friendly query language ("Rel") which is vaguely Datalogish, but a bit more flexible. The key thing with them is it's designed for querying very large cloud backed datasets, high volumes of connected data. So maybe it's not as relevant here as I originally suggested. Re: marketing ... much of their marketing has shifted over the last two years to emphasizing the fact that it's a plugin thing for Snowflake, which wasn't their original MO. (There's an CMU DB talk they did some years ago that I thought was pretty brilliant and made me want to work there) My proposal about a datalog (or similar more high level declarative relational-model system) being useful here has to do with how it shifts the focus to logical propositions/rules and handles transitive joins etc naturally. It's a place an LLM could shove "facts" and "rules" it finds along the way, and then the system could join to find relationships. You can do this in SQL these days, but it isn't as natural or intuitive.
- ianbicking 1y agoThis looks like RAG...? That's fine, RAG is a very broad approach and there's lots to be done with it. But it's not distinct from RAG. Searching by embedding is just a way to construct queries, like ILIKE or tsvector. It works pretty nicely, but it's not distinct from SQL given pg_vector/etc. The more distinctive feature here seems to be some kind of proxy (or monkeypatching?) – is it rewriting prompts on the way out to add memories to the prompt, and creating memories from the incoming responses? That's clever (but I'd never want to deploy that). From another comment it seems like you are doing an LLM-driven query phase. That's a valid approach in RAG. Maybe these all work together well, but SQL seems like an aside. And it's already how lots of normal RAG or memory systems are built, it doesn't seem particularly unique...?
- mobilemidget 1y agoRAG, or Retrieval Augmented Generation, is an AI technique that improves large language models (LLMs) by connecting them to external knowledge bases to retrieve relevant, factual information before generating a response. This approach reduces LLM "hallucinations," provides more accurate and up-to-date answers, and allows for responses grounded in specialized or frequently updated data, increasing trust and relevance. I was unaware what RAG referred to, perhaps other too.
- codersfocus 1y agoSo HN is upvoting AI written ad slop now?
- paool 1y agoSaw this same "product" astroturfed on Reddit.
- vivzkestrel 1y agoHow does it compare to pgvector?
- Charon77 1y agoHave you considered using prolog as a database instead of mysql? Good ways to store relations, iterating weird combinations, filling the blanks
- zvr 1y agoI think Datalog would be even more suitable than Prolog for this use case.
- gdestus 1y agoThis is exactly the lesson we learned as well but didnt want to publish. Relational data stores are desperately underrated for LLM retrieval especially concerning things like personality and memory
- slake 1y agoSometimes I think we need to expose what 'memories' or their semantic representation has been stored in a 'memory store' so that humans can review and verify it over time. This will help the LLM 'forget' things that the humans using it don't really think is that relevant.