GitHub user yjhjstz added a comment to the discussion: [DISCUSSION] Making
Apache Cloudberry an Agent-Native Analytical Database
Great direction. I'd suggest referencing TiDB's "data agent" path when planning
this out — several pieces are highly relevant:
1. **MCP Server first**: TiDB's earliest concrete step was the open-source
TiDB MCP Server (https://docs.pingcap.com/ai/tidb-mcp-server/) (STDIO/SSE,
tools like `show_databases`, query,
execute) rather than internal database changes. Since Cloudberry already has
an MCP server, adding `vector_search`/`hybrid_search` tools as Phase 0 would
validate demand faster than
waiting for the full Lance FDW.
2. **Agent memory / write-back**: PingCAP argues agent memory must write back
(https://www.pingcap.com/blog/agent-memory-write-back-database/) —
retrieval-only memory accumulates
contradictions; you need an ACID engine to read, join, and write corrected
state. This maps to the "Analytical RAG" section: results of AI
extraction/classification need somewhere
durable to land. Read-only FDW is the right Phase 1, but a later roadmap item
could be writing extracted features back into Cloudberry tables.
3. **Extreme multi-tenancy**: TiDB X targets millions of logical tenants on
object storage for agent workloads. If Cloudberry aims to be the analytical
backend for many concurrent
agents, how to manage many small ephemeral datasets/vector sets is worth
borrowing from.
In short, TiDB's path is roughly "MCP interface → agent memory write-back →
multi-tenant architecture," which complements the "FDW → vector pushdown →
analytical RAG → MCP tools"
roadmap here — validate via MCP tools first, then decide how deep the Lance
integration should go.
GitHub link:
https://github.com/apache/cloudberry/discussions/1967#discussioncomment-18334762
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