was listening to the latent space podcast and they mentioned this open source thing called octopoda. honestly i was bracing for the usual slop, but fairly impressed, what are other peoples impressions (agent builders)
spent the weekend trying it because i'm rebuilding my multi agent setup and was tired of the glue code i had between langchain, chroma, and redis just to give five agents anything resembling persistent memory.
first surprise was the install. pip install octopoda. that was it. no docker, no separate vector db, no redis. imported AgentRuntime and my five agents (researcher, writer, code reviewer, categoriser, coordinator) had memory. i kept waiting for the "now configure your backend" step that never came.
second surprise came about three hours in. i hard killed the python process mid run to test something, restarted it, and the agents still remembered everything. full history, facts they'd extracted, all the categorisations my triage agent had made. coming from a setup where i was manually checkpointing state every few minutes that was genuinely weird in a good way.
the thing that actually got me was the loop detection. one of my code review agents got stuck retrying a failing api call about twelve times. i didn't even notice because it was running in the background. octopoda's detector flagged it within seconds. ran the numbers afterwards, would have burned around eight bucks in tokens before i'd have checked the openai bill at end of day. paid for itself in one weekend.
semantic search just kind of works too. agent.recall_similar("when does the user usually deploy") returned the right context across agents and i hadn't wired up any embedding pipeline. embeddings are computed locally apparently, so no extra api calls for that bit. the cross agent memory sharing thing surprised me too, my writer can read what the researcher stored without anything explicit. could see that being too magic for some people but for me it cut a lot of orchestration code i'd otherwise have to maintain.
also weirdly polished dashboard. i half expected a 2014 bootstrap template with three working buttons and a pricing page that 404s. it actually has a 3d brain visualisation of agent activity, which is more cool than useful but i caught myself watching it for like five minutes.
i'm sitting at about a hundred thousand memories across the five agents which is nothing. curious if anyone here has actually gone bigger. how does shared memory between agents hold up past a million entries? does semantic search start to slow down or stay reasonable? has anyone hit a wall on the audit logging at scale? also keen to hear from anyone running this in production, what's broken for you, what's the worst failure mode you've hit, what do you wish was different.
drop your stack if you've used it. especially curious about anyone running it alongside crewai or autogen since i'm thinking about migrating my crewai project over but want to know what i'm walking into first.