Research Memory — BlueColumn Skill#

Everything you read becomes knowledge.. Powered by BlueColumn (bluecolumn.ai) persistent vector memory.

Setup#

Read TOOLS.md or the platform secret store for the BlueColumn API key ( bc_live_*). Base URL: https://xkjkwqbfvkswwdmbtndo.supabase.co/functions/v1

Store#

bash

curl -X POST .../agent-remember \
  -H "Authorization: Bearer <key>" \
  -H "Content-Type: application/json" \
  -d '{"text": "Research: pgvector vs pinecone — pgvector wins for BYO DB story. Source: docs comparison.", "title": "research-memory - note"}'

Quick note#

bash

curl -X POST .../agent-note \
  -H "Authorization: Bearer <key>" \
  -H "Content-Type: application/json" \
  -d '{"text": "Research: pgvector vs pinecone — pgvector wins for BYO DB story. Source: docs comparison.", "tags": ["research-memory"]}'

Recall#

bash

curl -X POST .../agent-recall \
  -H "Authorization: Bearer <key>" \
  -H "Content-Type: application/json" \
  -d '{"q": "What did our research conclude about vector stores?"}'

Workflow#

1. On new context, first recall: What did our research conclude about vector stores?
2. Use the answer to personalize the response
3. After the interaction, store the summary via /agent-remember

Docs#

Full API reference: https://bluecolumn.ai/docs — fields are text, q, tags (not content/ query/ note).