The practical follow-up to the goldfish-memory post. Bring a Postgres database with pgvector and an agent that talks to users; an hour later you’ve got two-tier memory bolted on. Staging, realtime and consolidate cells, three scheduling options, three reader patterns, and an LLM fact extractor — Python and Rust both.
The hands-on follow-up to the why-I-built-it post. Real commands, real outputs: install Stele, wire it into your agent, store artifacts with citations, supersede facts, time-travel with as_of, stash oversized tool output, and run recall through two strategies. Five minutes to install, the rest is just typing.
Real OLTP corpus, twelve-combo bakeoff with three baked-in models plus Snowflake Arctic via BYO YAML, hybrid search via promoted metadata, then wired into a LangGraph agent through inline mode. Every command actually run.
Part 2 of 3. A complete Docker setup for Postgres 16 with pgvector and Apache AGE, plus your first vector similarity query and your first Cypher traversal — with the gotchas that cost me an afternoon the first time.
