[HN Gopher] Show HN: Grov - Multiplayer for AI coding agents
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       Show HN: Grov - Multiplayer for AI coding agents
        
       Hi HN, I'm Tony.  I built Grov (https://grov.dev/) because I hit a
       wall with current AI coding assistants: they are "single-player."
       The moment I kill a terminal pane or close a chat session, the
       high-level reasoning and architectural decisions generated during
       that session are lost. If a teammate touches that same code an hour
       later, their agent has to re-derive everything from scratch or read
       many documentation files for basically any feature implemented or
       bug fixed.  I wanted to stop writing a lot of docs for everything
       just to give context to my agents or have to re-explain to my
       agents what my teammate did and why.  Grov is an open-source
       context layer that effectively gives your team's AI agents a
       shared, persistent memory.  Here is the technical approach:  1.
       Decision-grain memory, not document storage: When you sync a
       memory, Grov structures knowledge at the decision level. We capture
       the specific aspect (e.g., "Auth Strategy"), the choice made
       ("JWT"), and the reasoning ("Stateless for scaling"). Crucially,
       when your codebase evolves, we don't overwrite memories, we mark
       old decisions as superseded and link them to the new choice. This
       gives your team an audit trail of architectural evolution, not just
       the current snapshot.  2. Git-like branches for memories: Teams
       experimenting with different approaches can create memory branches.
       Memories on a feature branch stay isolated until you are ready to
       merge. Access control mirrors Git: main is team-wide, while feature
       branches keep noise isolated. When you merge the branch, those
       accumulated insights become instantly available to everyone's
       agents.  3. Two-stage injection (Token Optimization): The expensive
       part of shared memory isn't storage it's the context window.
       Loading 10 irrelevant memories wastes tokens and confuses the
       model. Grov uses a "Preview - Expand" strategy: Preview: A hybrid
       semantic/keyword search returns lightweight memory summaries (~100
       tokens). Expand: The full reasoning traces (~500-1k tokens) are
       only injected if the agent explicitly requests more detail. This
       typically results in a 50-70% token reduction per session compared
       to raw context dumping.  The result: Your teammate's agent doesn't
       waste 5 minutes re-exploring why you chose Postgres over Redis, or
       re-reading auth middleware. It just knows, because your agent
       already figured it out and shared it.  Github:
       https://github.com/TonyStef/Grov
        
       Author : tonyystef
       Score  : 17 points
       Date   : 2026-01-21 21:40 UTC (1 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | dang wrote:
       | [under-the-rug stub - see
       | https://news.ycombinator.com/item?id=45988611 for explanation]
       | 
       | [guys, don't do this! HN will flame you for it and it will ruin
       | your otherwise fine Show HN thread]
        
         | ambersahdev wrote:
         | Do you deal with memory compaction yourself or let the models
         | handle it?
        
           | tonyystef wrote:
           | We let the models handle it, we don't compact for them.
        
         | dolevalgam wrote:
         | I really need this with all the sessions open
        
         | davelradindra wrote:
         | Very useful.
        
         | sintem wrote:
         | dope. let me give it a go.
        
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       (page generated 2026-01-21 23:00 UTC)