In Honcho
You want a theory-of-mind user model — how someone reasons, not just facts.
Comparison
nlqdb vs Honcho
Pick Honcho if you want a memory layer that models how each user reasons — communication style, decision patterns — for personalization. Pick nlqdb if your agent also needs to run analytical queries (counts, group-bys, reports) over the structured rows it remembers.
The same goal, two ways.
> users by plan tier who completed onboarding this month, most recent first
In your HTML
<nlq-data goal="users by plan tier who completed onboarding this month, most recent first"></nlq-data>The aggregate slice over remembered rows is nlqdb's lane; Honcho's lane is modelling how each user reasons, not GROUP BY over what they did.
What's different
Four dimensions that decide it
| Dimension | nlqdb | Honcho | Note |
|---|---|---|---|
| Structured rows + typed columns | Honcho stores messages + peer representations in pgvector; nlqdb stores typed rows in Postgres. | ||
| Natural-language recall / queries | |||
| Aggregations + reporting (COUNT, GROUP BY, JOIN) | Honcho's `peer.chat()` reasons over context; it has no SQL aggregation over stored facts. | ||
| Theory-of-mind / dialectic user modeling | Honcho builds a model of how each peer reasons; nlqdb stores and queries what they did. |
Show 4 more rows
| Dimension | nlqdb | Honcho | Note |
|---|---|---|---|
| Hybrid search (BM25 + vector) over messages | |||
| MCP server (agent-callable) | |||
| Auto-migration via NL ('add a `tier` column') | |||
| Open source / self-hostable | Honcho is AGPL-3.0 with a self-hostable FastAPI server; nlqdb is FSL 1.1 (source-available) with no GA self-host container yet. |
shipped · partial · not shipped
When to choose nlqdb
- Your agent stores structured rows it later counts, groups, and reports over in SQL.
- You want one MCP server that provisions Postgres, stores rows, and answers aggregations.
- The schema must evolve as the agent learns — 'add a `tier` column'.
- You need exact, typed answers — not a probabilistic model of how a user thinks.
When to choose Honcho
- You want a theory-of-mind user model — how someone reasons, not just facts.
- Personalization across sessions matters more than SQL aggregation over stored rows.
- Your memory is conversational — sessions, peers, and evolving representations of people.
- An AGPL-3.0, self-hostable memory server is on your shortlist.
Questions buyers ask
- Can I use Honcho for user modeling and nlqdb for analytics over the same agent?
- Yes — they're complementary. Honcho answers 'how does this user prefer to be addressed and what do they tend to decide'; nlqdb answers 'how many tasks did each user complete per week this quarter'. Both sit behind one MCP-aware agent; nlqdb's MCP server exposes `nlqdb_query`, which materialises Postgres on first reference, so the structured side self-provisions.
- Does nlqdb model how a user thinks the way Honcho does?
- No, and it doesn't try to. Honcho's dialectic user modeling builds an evolving theory-of-mind representation of each peer — reasoning style, communication patterns — for personalization. nlqdb's lane is exact, aggregate SQL over the structured rows an agent stores, which a user model doesn't give you.
- Is nlqdb a vector database like the pgvector store under Honcho?
- No. nlqdb is Postgres-first for relational SQL over typed rows (ClickHouse for analytical engines in Phase 2). Honcho keeps vector-embedded collections per observer/observed peer pair for hybrid recall; nlqdb's contract is COUNT, GROUP BY, and JOIN over rows, not similarity search.
- How does my agent provision an nlqdb database autonomously?
- The MCP server exposes `nlqdb_query` — your agent calls it with a goal in English, the server materialises Postgres plus schema on first reference, and returns the answer bound to the agent's tenant. There's no separate create-DB verb to call first.
- Is nlqdb open source like Honcho?
- Not in the same way. Honcho is AGPL-3.0 with a self-hostable FastAPI server you can run via Docker. nlqdb's source is available under FSL 1.1, but there's no GA self-host container yet — the platform is hosted-only for now.
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