bluecolumn-memory

Store and recall persistent memory for AI agents using BlueColumn. Agents can save facts, decisions, preferences, promises, and action items, then query them later with natural language. Installable via npx skills add bluecolumnconsulting-lgtm/bluecolumn-agent-skill or copy into any agent's skill directory.

BlueColumn Memory (agent-ready)

BlueColumn gives AI agents persistent, searchable memory. Store anything (facts, decisions, preferences, promises, action items, transcripts), then recall it later with natural-language queries — with citations back to the source.

This is the agent-facing skill. It works with any assistant that can call HTTP APIs (OpenClaw, Claude Code, Cursor, or raw HTTP).

Setup

1. Get an API key at bluecolumn.ai/login. Create an account; onboarding auto-provisions your first bc_live_... key. Copy it from Dashboard → API Keys.
   • Free tier: 100 writes + 100 reads + 30 audio minutes per month — no credit card required.
2. Store the key wherever your agent keeps secrets (env var, secret store, or a private local notes file). Never commit it to a public repo. Keys look like bc_live_... (32+ chars after the prefix).

Endpoints

Base URL: https://api.bluecolumn.ai

Action	Endpoint
Store memory	POST /remember
Query memory	POST /recall
Quick observation	POST /note

(Equivalent aliases also work: /agent-remember, /agent-recall, /agent-note.)

1. RECALL — query memory

text

POST /recall
Content-Type: application/json
Authorization: Bearer <api-key>

Body:

json

{ "q": "What did the user decide about the billing design?" }

Returns: AI-synthesized answer with source citations (session ids, timestamps, relevance scores).

2. REMEMBER — store memory

text

POST /remember
Content-Type: application/json
Authorization: Bearer <api-key>

Body:

json

{
  "text": "Full raw content to store (transcript, doc, note...)",
  "title": "Topic label, e.g. Meeting - Q3 planning - 2026-08-27"
}

Returns: session_id, chunk count, auto-extracted summary, action_items, key_topics.

3. NOTE — quick observation (lightweight)

text

POST /note
Authorization: Bearer <api-key>

Body:

json

{
  "text": "User prefers concise replies with concrete next steps",
  "tags": ["preference"]
}

Short vector note for preferences and quick observations (min 5 chars). Optional tags.

Field-name gotchas

• /remember takes text, not content
• /recall takes q, not query
• /note takes text, not note

Billing & limits — what to do when a call fails

All calls are metered against the account's plan. Handle these status codes:

Status	Meaning	Agent action
401	Invalid or missing key	Re-check the key is set and active.
402	Free tier exhausted / insufficient credit	Stop making billable calls and tell the user to top up at the URL in the response (topup_url).
429	Plan quota reached	Tell the user to upgrade at bluecolumn.ai/pricing.
400	Bad input	Fix the body per the schema above and retry once.

Response envelope: { "error": { "code": "insufficient_credit", "message": "...", "topup_url": "..." }, "request_id": "..." }.

Free tier: 100 writes + 100 reads + 30 audio minutes/month. Pay-as-you-go beyond that. When you hit the limit mid-task, surface the upgrade URL to the user — do not silently retry or switch keys.

Usage pattern (skill-style)

• Store a transcript: REMEMBER title="YouTube - <title> <date>" text=<full text>
• Ask a question: RECALL q="What are the key action items from the last planning session?"
• Save a decision: NOTE text="User confirmed: deploy on Fridays only after smoke tests pass" tags=["decision"]

Why agents should use this

• Memory persists across sessions — no context loss.
• Synthesized answers with citations — verify claims back to source.
• Auto-extracted summaries and action items on every write.
• Simple REST surface — works from any language or agent framework.

Related

• MCP server (Claude Desktop / Claude Code / Cursor): npx bluecolumn-mcp — github.com/bluecolumnconsulting-lgtm/bluecolumn-mcp
• Docs: bluecolumn.ai/docs
• Pricing: bluecolumn.ai/pricing
• ClawHub: the bluecolumn-memory skill