SQ

SQLite for Rivet Actors – one database per agent, tenant, or document

Hacker News

SQLite for Rivet Actors – one database per agent, tenant, or document

Hey HN! We posted Rivet Actors here previously [1] as an open-source alternative to Cloudflare Durable Objects. Today we've released SQLite storage for actors (Apache 2.0). Every actor gets its own SQLite database. This means you can have millions of independent databases: one for each agent, tenant, user, or document. Useful for: - AI agents: per-agent DB for message history, state, embeddings - Multi-tenant SaaS: real per-tenant isolation, no RLS hacks - Collaborative documents: each document gets its own database with built-in multiplayer - Per-user databases: isolated, scales horizontally, runs at the edge The idea of splitting data per entity isn't new: Cassandra and DynamoDB use partition keys to scale horizontally, but you're stuck with rigid schemas ("single-table design" [3]), limited queries, and painful migrations. SQLite per entity gives you the same scalability without those tradeoffs [2]. How this compares: - Cloudflare Durable Objects & Agents: most similar to Rivet Actors with colocated SQLite and compute, but closed-source and vendor-locked - Turso Cloud: Great platform, but closed-source + diff use case. Clients query over the network, so reads are slow or stale. Rivet's single-writer actor model keeps reads local and fresh. - D1, Turso (the DB), Litestream, rqlite, LiteFS: great tools for running a single SQLite database with replication. Rivet is for running lots of isolated databases. Under the hood, SQLite runs in-process with each actor. A custom VFS persists writes to HA storage (FoundationDB or Postgres). Rivet Actors also provide realtime (WebSockets), React integration (useActor), horizontal scalability, and actors that sleep when idle. GitHub: https://github.com/rivet-dev/rivet Docs: https://www.rivet.dev/docs/actors/sqlite/ [1] https://news.ycombinator.com/item?id=42472519 [2] https://rivet.dev/blog/2025-02-16-sqlite-on-the-server-is-mi... [3] https://www.alexdebrie.com/posts/dynamodb-single-table/

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
80%80% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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