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/ZepFPMSRemembers conversationsYesYes (via prompt rules)Tracks tasks & projectsNoYesAlerts you about stuck workNoYes (heartbeat)Manages token budgetNoYes (L0/L1/L2)Parent-child task hierarchyNoYesGitHub integrationNoYes

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)#

ToolWhat it doesbootstrapLoad memory context (call at conversation start)heartbeatScan for risks: blocked, stale, at-risk taskscreate_nodeCreate a project/task/goalupdate_statusChange status (inbox/active/waiting/done/dropped)append_logRecord decisions, progress, linksget_context_bundleGet token-budgeted context payloadsearch_nodesFind tasks by status, parent, sourceget_nodeGet full details of a work itemshift_focusSwitch AI attention to a specific taskexpand_contextSee parent, children, dependenciesupdate_fieldUpdate title, summary, deadline, etc.attach_node / detach_nodeMove tasks in hierarchyadd_dependency / remove_dependencyManage task dependenciesunarchiveRestore completed/dropped tasksset_persistentProtect tasks from auto-archiveget_assembly_traceDebug 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