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Show HN: Recall: Give Claude memory with Redis-backed persistent context (npmjs.com)

171 points by elfenleid ↗ HN
Hey HN! I'm José, and I built Recall to solve a problem that was driving me crazy.

The Problem: I use Claude for coding daily, but every conversation starts from scratch. I'd explain my architecture, coding standards, past decisions... then hit the context limit and lose everything. Next session? Start over.

The Solution: Recall is an MCP (Model Context Protocol) server that gives Claude persistent memory using Redis + semantic search. Think of it as long-term memory that survives context limits and session restarts.

How it works: - Claude stores important context as "memories" during conversations - Memories are embedded (OpenAI) and stored in Redis with metadata - Semantic search retrieves relevant memories automatically - Works across sessions, projects, even machines (if you use cloud Redis)

Key Features: - Global memories: Share context across all projects - Relationships: Link related memories into knowledge graphs - Versioning: Track how memories evolve over time - Templates: Reusable patterns for common workflows - Workspace isolation: Project A memories don't pollute Project B

Tech Stack: - TypeScript + MCP SDK - Redis for storage - OpenAI embeddings (text-embedding-3-small) - ~189KB bundle, runs locally

Current Stats: - 27 tools exposed to Claude - 10 context types (directives, decisions, patterns, etc.) - Sub-second semantic search on 10k+ memories - Works with Claude Desktop, Claude Code, any MCP client

Example Use Case: I'm building an e-commerce platform. I told Claude once: "We use Tailwind, prefer composition API, API rate limit is 1000/min." Now every conversation, Claude remembers and applies these preferences automatically.

What's Next (v1.6.0 in progress): - CI/CD pipeline with GitHub Actions - Docker support for easy deployment - Proper test suite with Vitest - Better error messages and logging

Try it:

npm install -g @joseairosa/recall # Add to claude_desktop_config.json # Start using persistent memory

39 comments

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jcmontx
If this delivers can be 100% game changer, I will try it out and give some feedback
bryanhogan
Why would you not use context files in form of .md? E.g. how the SpecKit project does it.
pacoWebConsult
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?
fennecbutt
Yes, exactly this. But idiot VC funding (which YC is also somewhat engaged in I imagine) cries for MCP. Hence multi billion valuations and many million dollar salaries and bonuses being thrown around.

It's ridiculous and ties into the overall state of the world tbh. Pretty much given up hoping that we'll become an enlightened species.

So let's enjoy our stupid MCP and stupid disposable plastic because I don't see any way that we aren't gonna cook ourselves to extinction on this planet. :)

warthog
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

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tarun_anand
Claude introduced it's own memories api.. have you had a look?
mannyv
This is excellent for those of us who are building local AIs.
otterley
Does it work with Valkey as well?
h1fra
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?
alecco
Why not just ask CC to write a prompt or Markdown file to re-start the conversation in a new chat?
the_arun
I wish there was a way to send compressed context to LLMs instead of plain text. This will reduce token size, performance & operational costs.
iambateman
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.

asdev
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
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
Heh, I'm building the same thing this week (albeit with postgres rather than redis). I bet like 15% of the people here are.
iamleppert
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
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
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?

daxfohl
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.
gmerc
Every single persistent memory feature is a persistence vector for prompt injection.
DenisM
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
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?
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aktuel
Wouldn't the cache over time also be filled up with irrelevant and redundant information?
aiisthefiture
With redis? Why?
elfenleid
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
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.