Fpms Memory

Cognitive memory engine — gives your AI persistent work tracking, proactive risk alerts, and cross-conversation continuity. Never lose track of projects again.

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

	Mem0/Zep	FPMS
Remembers conversations	Yes	Yes (via prompt rules)
Tracks tasks & projects	No	Yes
Alerts you about stuck work	No	Yes (heartbeat)
Manages token budget	No	Yes (L0/L1/L2)
Parent-child task hierarchy	No	Yes
GitHub integration	No	Yes

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

Setup

1. Install

bash

pip install focalpoint

2. Add MCP server to openclaw.yaml

yaml

mcp_servers:
  fpms:
    command: focalpoint

3. Restart OpenClaw

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

Use Cases

Project tracking

text

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

text

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

text

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)

Tool	What it does
bootstrap	Load memory context (call at conversation start)
heartbeat	Scan for risks: blocked, stale, at-risk tasks
create_node	Create a project/task/goal
update_status	Change status (inbox/active/waiting/done/dropped)
append_log	Record decisions, progress, links
get_context_bundle	Get token-budgeted context payload
search_nodes	Find tasks by status, parent, source
get_node	Get full details of a work item
shift_focus	Switch AI attention to a specific task
expand_context	See parent, children, dependencies
update_field	Update title, summary, deadline, etc.
attach_node / detach_node	Move tasks in hierarchy
add_dependency / remove_dependency	Manage task dependencies
unarchive	Restore completed/dropped tasks
set_persistent	Protect tasks from auto-archive
get_assembly_trace	Debug context assembly

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