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Fpms Memory

FPMS 为 AI 提供跨会话持久记忆：以项目→任务→子任务层级记录工作项，支持状态流转、决策日志与依赖管理，通过心跳扫描发现阻塞、停滞或临期任务；可按 token 预算加载上下文，并声称支持 GitHub Issues 同步，数据存储于本地 SQLite。

jeff0052
jeff0052
热度 43.83
最近更新 2026-05-11 17:34:55
分类：知识依赖：MCP 服务应用场景：任务管理 / 笔记整理 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add fpms-memory
Metadata
name
fpms-memory
description
Cognitive memory engine — gives your AI persistent work tracking, proactive risk alerts, and cross-conversation continuity. Never lose track of projects again.
version
0.2.0
metadata
{"openclaw":{"emoji":"🧠","homepage":"https://github.com/jeff0052/founderOSclaudecode","requires":{"bins":["python3"]},"install":[{"kind":"uv","package":"focalpoint","bins":["focalpoint"]}]}}

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

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)

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