db0 vs DIY stack
The most common DIY pattern is Redis for session state, Postgres with pgvector for long-term memory, and a background worker to bridge them. It works — until the glue code becomes the project.
The typical DIY architecture
According to the 2025 Stack Overflow Developer Survey, 43% of developers building AI agents use Redis for memory and data storage. The common architecture combines Redis for fast session context with Postgres + pgvector for long-term semantic recall.
The hybrid pattern works like this: agents write synchronously to Redis for current task state. A background worker flushes completed turns to Postgres with embedding vectors. On wake-up, working memory comes from Redis while episodic context comes from pgvector similarity search.
This architecture is sound. The problem is everything you have to build on top of it.
What the DIY stack requires you to build
| Component | DIY approach | db0 equivalent |
|---|---|---|
| Session state | Redis with custom key schema and TTLs | Session-scoped memory with automatic lifetime |
| Embeddings | pgvector + custom embedding pipeline | Built-in hybrid search (semantic + keyword) |
| Background sync | Custom worker flushing Redis → Postgres | Automatic extraction and persistence |
| Scope management | Custom tags or separate tables per scope | Four built-in scopes (user, agent, session, task) |
| Context packing | Custom logic to select and rank memories for the token budget | context().pack() with relevance scoring and budget-aware packing |
| Fact evolution | Overwrite rows or append-only with no conflict resolution | Superseding with audit trail — old facts preserved, excluded from search |
| State recovery | Build your own checkpoint/restore logic | state().checkpoint() / state().branch() / rollback |
| Sub-agent sharing | Manual memory passing between agent processes | harness.spawn() with shared user memory, isolated task memory |
Where DIY stacks break
Cost and operational overhead
Keeping full conversation histories in Redis drives 4-6x the RAM cost compared to hybrid approaches, per 1,000 agents. The hybrid pattern reduces this, but adds a background worker to monitor, a sync protocol to maintain, and two databases to operate.
db0 defaults to SQLite — a single local file, zero infrastructure. When you need multi-device sync, switch to Postgres. One backend, not two.
When a DIY stack makes sense
- You already have Redis and Postgres in production and want to reuse existing infrastructure.
- Your agent is simple enough that you only need key-value session state and basic vector search.
- You need fine-grained control over every aspect of the memory pipeline and are willing to maintain the glue code.
- Your team has deep operational experience with these systems and doesn't want another abstraction.
When db0 is the better fit
- You're building your second agent project and realized the memory abstractions from the first one aren't reusable.
- You need scoped lifetimes, fact evolution, and context assembly — not just storage and retrieval.
- You want to start local (SQLite, no infrastructure) and add Postgres later when you need sync.
- You spawn sub-agents that need shared user memory without manual serialization and handoff.
- You'd rather write agent logic than maintain embedding pipelines, cache invalidation, and sync workers.
DIY stacks are built for one agent at a time. The Redis key schema, the embedding pipeline, the scope tags, the context assembly logic — all of it is bespoke to that specific project.
db0 is the reusable layer. Same SDK, different backend, different agent. The scoping, superseding, and context assembly come with it.