7 ms·

Show HN: Recall: Give Claude memory with Redis-backed persistent context

• jcmontx 1y ago
  If this delivers can be 100% game changer, I will try it out and give some feedback
  • elfenleid 1y ago
    I've been using it for a while now, personally. I've found that I have less issues with context, I can easily recall (pun intended) after a context compact, etc.
• bryanhogan 1y ago
  Why would you not use context files in form of .md? E.g. how the SpecKit project does it.
  • elfenleid 1y ago
    I still do, but having this allows for strategies like memory decay for older information. It also allows for much more structured searching capabilities, instead of opening file which are less structured. .md files work great for small projects. But they hit limits: 1. Size - 100KB context.md won't fit in the window 2. No search - Claude reads the whole file every time 3. Manual - You decide what to save, not Claude 4. Static - Doesn't evolve or learn Recall fixes this: - Semantic search finds relevant memories only - Auto-captures context during conversations - Handles 10k+ memories, retrieves top 5 - Works across multiple projects Real example: I have 2000 memories. That's 200KB in .md form. Recall retrieves 5 relevant ones = 2KB. And of course, there's always the option to use both .md for docs, Recall for dynamic learning. Does that help?
    • bryanhogan 1y ago
      I'm not sure. You don't use a single context.md file, you use multiple and add them when relevant in context. AIs adjust these as you need, so they do "evolve". So what you try to achieve is already solved. These two videos on using Claude well explain what I mean: 1. Claude Code best practices: https://youtu.be/gv0WHhKelSE https://youtu.be/gv0WHhKelSE 2. Claude Code with Playwright MCP and subagents: https://youtu.be/xOO8Wt_i72s https://youtu.be/xOO8Wt_i72s
      • elfenleid 1y ago
        Yeah that's a solid workflow and honestly simpler than what I built - I think Recall makes sense when you hit the scale where managing multiple .md files becomes tedious (like 50+ conversations across 10 projects), but you're right that for most people your approach works great and is way less complex.
    • BHSPitMonkey 1y ago
• pacoWebConsult 1y ago
  Why would you bloat the (already crowded) context window with 27 tools instead of the 2 simplest ones: Save Memory & Search Memory? Or even just search, handling the save process through a listener on a directory of markdown memory files that Claude Code can natively edit?
  • elfenleid 1y ago
    That's a great point, the reality is that context, at least from personal experience, is brittle and over time will start to lose precision. This is a always there, persistent way for claude to access "memories". I've been running with it for about a week now and did not feel that the context would get bloated.
    • j45 1y ago
      I do notice building up context makes a difference. Having the context modular helps too.
  • fishmicrowaver 1y ago
    People are just ricing out AI like they rice out Linux, nvim or any other thing. It's pretty simple to get results from the tech. Use the CLI and know what you're doing.
    • j45 1y ago
      Fair points, share how you are learning - seems to be more than one way to the same result.
      • icedrop 1y ago
        Maintain a good agents.md with notes on code grammar/structure/architecture conventions your org uses, then for each problem, prompt it step-by-step as if you were a junior engineer's monologue. e.g. as I am dropped into a new codebase: 1. Ask Claude to find the section of code that controls X 2. Take a look manually 3. Ask it to explain the chain of events 4. Ask it to implement change Y, in order to modify X to do behavior we want 5. Ask it about any implementation details you don't understand, or want clarification on -- it usually self-edits well. 6. You can ask it to add comments, tests, etc., at this point, and it should run tests to confirm everything works as expected. 7. Manually step through tests, then code, to sanity check (it can easily have errors in both). 8. Review its diff to satisfaction. 9. Ask it to review its own diff as if it was a senior engineer. This is the method I've been using, as I onboard onto week 1 in a new codebase. If the codebase is massive, and READMEs are weak, AI copilot tools can cut down overall PR time by 2-3x. I imagine overall performance dips after developer familiarity increases. From my observation, it's especially great for automating code-finding and logic tracing, which often involves a bunch of context-switching and open windows--human developers often struggle with this more than LLMs. Also great for creating scaffolding/project structure. Overall weak at debugging complex issues, less-documented public API logic, often has junior level failures.
• warthog 1y ago
  imo it would be better to carry the whole memory outside of the inference time where you could use an LLM as a judge to track the output of the chat and the prompts submitted it would sort of work like grammarly itself and you can use it to metaprompt i find all the memory tooling, even native ones on claude and chatgpt to be too intrusive
  • elfenleid 1y ago
    Totally get what you're saying! Having Claude manually call memory tools mid-conversation does feel intrusive, I agree with that, especially since you need to keep saying Yes to the tool access. Your approach is actually really interesting, like a background process watching the conversation and deciding what's worth remembering. More passive, less in-your-face. I thought about this too. The tradeoff I made: Your approach (judge/watcher): - Pro: Zero interruption to conversation flow - Pro: Can use cheaper model for the judge - Con: Claude doesn't know what's in memory when responding - Con: Memory happens after the fact Tool-based (current Recall): - Pro: Claude actively uses memory while thinking - Pro: Can retrieve relevant context mid-response - Con: Yeah, it's intrusive sometimes Honestly both have merit. You could even do both, background judge for auto-capture, tools when Claude needs to look something up. The Grammarly analogy is spot on. Passive monitoring vs active participation. Have you built something with the judge pattern? I'd be curious how well it works for deciding what's memorable vs noise. Maybe Recall needs a "passive mode" option where it just watches and suggests memories instead of Claude actively storing them. That's a cool idea.
    • westurner 1y ago
      Is this the/a agent model routing problem? Which agent or subagent has context precedence? jj autocommits when the working copy changes, and you can manually stage against @-: https://news.ycombinator.com/item?id=44644820 https://news.ycombinator.com/item?id=44644820 OpenCog differentiates between Experiential and Episodic memory; and various processes rewrite a hypergraph stored in RAM in AtomSpace. I don't remember how the STM/LTM limit is handled in OpenCog. So the MRU/MFU knapsack problem and more predictable primacy/recency bias because context length limits and context compaction?
• deleted 1y ago
  [deleted]
• tarun_anand 1y ago
  Claude introduced it's own memories api.. have you had a look?
  • elfenleid 1y ago
    Yes I did, I worked on this a while back, before it was availabale I believe. I'll have another check. Thanks for the heads up
• mannyv 1y ago
  This is excellent for those of us who are building local AIs.
  • elfenleid 1y ago
    That's a great point! And also works really well for shared context between claude instances, for example, we use that for our business model in the company, all business rules and model is stored as memories in a central redis that the mcp connects to. The way that memories are stored are specific to a folder or global (similar to CLAUDE.md home directiory), but with this approach you can have an external redis where multiple claudes read and write into as a shared almost hive like memory.
• otterley 1y ago
  Does it work with Valkey as well?
  • elfenleid 1y ago
    Yep! Valkey should work fine. Recall just uses basic Redis commands - HSET, SADD, ZADD, etc. Nothing fancy. Valkey is Redis-compatible so all those commands work the same. I haven't tested it personally but there's no reason it wouldn't work. The Redis client library (ioredis) should connect to Valkey without issues. If you try it and hit any problems let me know! Would be good to officially support it.
• h1fra 1y ago
  I'm not super familiar with context and "memory", but adding context manually or via memory doesn't end up consuming context length either way?
  • elfenleid 1y ago
    Yeah it still uses context but way more efficiently, instead of injecting a 50KB context.md every time, Recall searches 10k memories and only injects the top 5 relevant ones (maybe 2KB), so you can store way more total knowledge.
• alecco 1y ago
  Why not just ask CC to write a prompt or Markdown file to re-start the conversation in a new chat?
  • elfenleid 1y ago
    Yeah people do that but it doesn't scale, after a while your "restart prompt" is 50KB and won't fit, plus you're stuck copying stuff manually instead of just asking "what did we say about Redis" and getting the relevant bits automatically.
• the_arun 1y ago
  I wish there was a way to send compressed context to LLMs instead of plain text. This will reduce token size, performance & operational costs.
  • joshstrange 1y ago
    > This will reduce token size, performance & operational costs. How? The models aren't trained on compressed text tokens nor could they be if I understand it correctly. The models would have to uncompress before running the raw text through the model.
    • the_arun 1y ago
      That is what I am looking for. a) LLMs are trained using compressed text tokens and b) use compressed prompts. Don't know how..but that is what I was hoping for.
      • deepdarkforest 1y ago
        The whole point of embeddings and tokens are that they are a compressed version of text, a lower dimensionality. now, how low depends on performance, lower amount of vectors=more lossy (usually). https://huggingface.co/spaces/mteb/leaderboard https://huggingface.co/spaces/mteb/leaderboard You can train your own with very very compressed, i mean you could even go down to each token=just 2 float numbers. It will train, but it will be terrible, because it can essentially only capture distance. Prompting a good LLM to summarize the context is probably funnily enough the best way of actually "compressing" context
      • rattyJ2 1y ago
        Tokens are already compressed. That's what tokenisation is.
• iambateman 1y ago
  I’ve started asking Claude to write tutorials that live in a _docs folder alongside my code. Then it can reference those tutorials for specific things. Interested in giving this a shot but it feels like a lot of infrastructure.
  • zzzeek 1y ago
    Yeah this is what I do, you want the knowledge in md files , but currently you don't want to stuff up the context with everything you know every time. I may be wrong here but my impression is the way that "context" is special and very limited in size vs "things the LLM is trained on" is still an unsolved problem getting AI to act like an "assistant" , AFAICT.
• asdev 1y ago
  The problem is you need to tell prompt Claude to "Store" or "Remember", if you don't it will never call the MCP server. Ideally, Claude would have some mechanism to store memories without any explicit prompting but I don't think that's currently possible today.
  • elfenleid 1y ago
    I've been experimenting with that in the last couple of days. I added to CLAUDE.md a directive on how and when to use recall and he's autoamtically calling the tool for store and fetch
• jMyles 1y ago
  Heh, I'm building the same thing this week (albeit with postgres rather than redis). I bet like 15% of the people here are.
  • _joel 1y ago
    Yep, me too. I've taken the reference memory mcp that anthropic release and bolted on pgsql, but with a bunch of other features that are specific to the app I'm building. Like user segmentation/isolation with RLS (app is multiuser) and some other entity relationship tracking things.
• iamleppert 1y ago
  I'm not seeing how this is any different than a standard vector database MCP tool. It's not like Claude is going to know about any of the things you told it to "remember" unless you explicitly tell it to use its memory tool like shown in the demo, to remember something you've stored.
• bananapub 1y ago
  how did you benchmark this against much less convoluted solutions, like "a text file"? how much better was this to justify all that extra complexity?
• datadrivenangel 1y ago
  How does Claude know when to try and remember? Often memory works too well and crowds out new things, so how are you balancing that?
  • datadrivenangel 1y ago
    Some of the other similar tools just arbitrarily pick the 3,5 or 10 most relevant memory results, which seems awkward.
• daxfohl 1y ago
  I'm surprised Anthropic doesn't offer something like this server-side, with an API to control it. Seems like it'd be a lot more efficient than having client manually reworking the context and uploading the whole thing.
  • ryan29 1y ago
    Who should own the context? Imagine having 20 years of context / memories and relying on them. Wouldn't you want to own that? I can't imagine pay-per-query for my real memories and I think that allowing that for AI assisted memory is a mistake. A person's lifetime context will be irreplaceable if high quality interfaces / tools let us find and load context from any conversation / session we've ever had with an LLM. On the flip side of that, something like a software project should own the context of every conversation / session used during development, right? Ideally, both parties get a copy of the context. I get a copy for my personal "lifetime context" and the project or business gets a copy for the project. However, I can't imagine businesses agreeing to that. If LLMs become a useful tool for assisting memory recall there's going to be fighting over who owns the context / memories and I worry that normal people will lose out to businesses. Imagine changing jobs and they wipe a bunch of your memory before you leave. We may even see LLM context ownership rules in employment agreements. It'll be the future version of a non-compete.
    • daxfohl 1y ago
      Whoever is paying for it? If you've got personal stuff you'd keep it in your own account (or maintain it independently), separate from your work account.
  • _joel 1y ago
    They do, new feature, not available in claude code but via API headers. https://docs.claude.com/en/docs/agents-and-tools/tool-use/memory-tool https://docs.claude.com/en/docs/agents-and-tools/tool-use/me...
    • daxfohl 1y ago
      That's still client side though. Seems like if they made it server-side it'd require fewer round trips.
• gmerc 1y ago
  Every single persistent memory feature is a persistence vector for prompt injection.
• DenisM 1y ago
  Imho you would have an easier sell if you separate knowledge into tiers: 1)overall design 2) coding standards 3) reasoning that lead to design 4) components and their individual structure 5) your current issue 6) etc Your project becomes progressively more valuable the further you go down the list. The overall design should be documented and curated to onboard new hires. Documenting current issues is a waste of time compared to capturing live discussion, so Recall is super useful here.
• jumski 1y ago
  Memory is hard! I'm very curious how the version history approach is working for you? Have you considered an age when retrieving? Is model supposed to manage the version history on its own? Is the semantic search used to help with that?
• deleted 1y ago
  [deleted]
• aktuel 1y ago
  Wouldn't the cache over time also be filled up with irrelevant and redundant information?
• aiisthefiture 1y ago
  With redis? Why?
  • elfenleid 1y ago
    No particular specific reason. I was working with another project that also had redis and decided to start with it. It can be changed to other tool, which one would you recommend?
• ffsm8 1y ago
  The memory feature I'd like to have would need built-in support from anthropic It'd be essentially 1. Language server support for lookups & keeping track of the code 2. Being able to "pin" memories to functions, classes, properties etc via the language server support/providing this context whenever changes are made in this function/class/properties etc, but not kept, so all following changes outside of that will no longer include this context (basically, changes that touch code with which memories will be done by agents with additional context, and only the results are synced back, not the way to achieve it) 3. Provide a ide integration for this context so you can easily keep track of what's available just by moving the cursor to the point the memory is pinned at Sadly impossible to achieve via MCP.
• DenisM 1y ago
  I think everyone concluded at this point that we need to improve models memory capabilities, but different people take different approach. My experience is that ChatGPT can engage in a very thoughtful conversations but if I ask for a summary it makes something very generic, useful to an outsider, but it does not catch salient points which were the most important outcomes. Did you notice the same problem?
  • elfenleid 1y ago
    I think that's a great point really. There is not 1 size fits all, different people will have different strategies that better suit their workflow.
• uncletoxa 1y ago
  Do you think any vector db would work better than redis?
  • elfenleid 1y ago
    I think that's a great point. I will experiment with different approaches. I started with redis mostly because it's something I have experience with and was a quick setup win, but having different strategies I think it could make sense.
• moomoo11 1y ago
  Throwing it out there, not sure how well it'd work but what about using OpenSearch + vector? AI can already form the query DSL quite nicely especially if it knows the indexes. I set up AI powered search this way, and it works really well with any open ended questions.
• ra 1y ago
  I built something similar but now use Codex instead. Using the VS Code extension you get dynamic context management which works really well. They also have a memory system built using reflexion (someone please correct me if I'm wrong) so proper evals are derived from lessons before storing.
• deleted 1y ago
  [deleted]
• btbuildem 1y ago
  A great hack/shortcut for solving this "memory" problem is to have a rolling RAG KB. You don't fill up the context, and you can use a re-ranking model to further improve accuracy. Aside from all that, using npm for distribution makes this a total non-starter for me.
  • elfenleid 1y ago
    Totally, point taken. I'll dig a bit deeper into that.
• nibab 1y ago
  Isn’t that what agents.md or Claude.md is for?
  • elfenleid 1y ago
    Absolutely! But this is not a replacement of those files, this is a different (better?) way to navigate through those learnings instead of having to read whole files.
• thund 1y ago
  Seems overkill when you can simply tell agents to do that automatically
• dfee 1y ago
  The code is written by Claude, the README is written by Claude, this HN post is written by Claude. My God, there’s no signal. It’s all noise.
  • replwoacause 1y ago
    Yes and it’s happening everywhere, not just this post
• Merad 1y ago
  I built a memory tool about 6 months while playing with MCP, it was based on a SQLite db. My experience then was that Claude wasn't very good at using the tools. Even with instructions to be proactive about searching memory and saving new memories it would rarely do so. Once you did press it to be sure to save memories it would go overboard, basically saving every message in the conversation as a memory. Are seeing more success in getting natural and seamless usage of the memory tools? IIRC at the time I was testing with Sonnet 3.7, I haven't tried it on the newer models. Repo here: https://github.com/mbcrawfo/KnowledgeBaseServer https://github.com/mbcrawfo/KnowledgeBaseServer
  • CSSer 1y ago
    It is really weird how some sessions with claude are better than others despite similar tasks. I'm certain it's not sleep deprivation or something else. Sometimes it gets on a hot streak by accidentally discovering the right tools to use. It's like an unstable solder joint or something. It's very difficult to guide it. When you do it overfits hard.