FPMS — Focal Point Memory System

Your AI forgets everything between conversations. FPMS fixes that.

Not just memory — attention management. FPMS tracks your projects, detects stuck tasks, and loads the right context at the right time.

What You Get

• Cross-conversation memory — Start Monday, continue Wednesday, review Friday
• Structured work tracking — Projects → Tasks → Subtasks with status lifecycle
• Proactive alerts — "Task X has been blocked for 3 days"
• Smart context loading — Only loads what fits your token budget
• GitHub sync — Issues auto-sync as FPMS nodes

How It's Different From Other Memory Tools

Other tools remember what was said. FPMS manages what needs to be done.

Setup

1. Install

pip install focalpoint

2. Add MCP server to openclaw.yaml

mcp_servers:
  fpms:
    command: focalpoint

3. Restart OpenClaw

That's it. 18 tools are now available in your conversations.

Use Cases

Project tracking

You: "Create a project for the product launch with 3 tasks"
(Next day)
You: "What's the launch status?"
AI:  "3 tasks: 1 done, 1 active, 1 blocked. The blocked task
      is waiting on design review — it's been 2 days."

Decision memory

You: "We're going with Stripe for payments"
(Two weeks later)
You: "Why did we pick Stripe?"
AI:  "You decided on March 15 — better API and lower international fees."

Risk detection

AI automatically runs heartbeat and finds:
  - Deploy task BLOCKED for 4 days
  - Docs update STALE — no activity for a week
  - Bug fix AT RISK — deadline is tomorrow

Available Tools (18)

Automatic Memory Rules

Follow these rules in EVERY conversation:

1. Conversation start → Call bootstrap to load memory
2. User mentions a task/project/goal → create_node
3. User makes a decision → append_log to the relevant node
4. Task progresses → update_status
5. Before conversation ends → append_log key takeaways
6. Every ~10 min → heartbeat to check for risks

Requirements

• Python 3.10+
• No external services — runs 100% locally on SQLite

Links

• GitHub
• PyPI
• Full Documentation