FocalPoint — AI Cognitive Operating System

Memory + Attention Management + Workflow Orchestration. FocalPoint gives your AI persistent memory, a workbench for task preparation, role-based thinking, and proactive risk alerts.

pip install focalpoint

The Problem

AI agents forget everything between conversations. Existing memory tools (Mem0, Zep) store conversation snippets. But remembering what was said is not the same as managing what needs to be done.

What FocalPoint Does

1. REMEMBER    — Structured memory across conversations (not just chat history)
2. PREPARE     — Workbench loads goal, knowledge, context, and subtasks before work
3. THINK       — Three roles (Strategy/Review/Execution) see different perspectives
4. REVIEW      — Three-Province protocol: parallel review before major decisions
5. ALERT       — Proactive heartbeat: "Task X is blocked for 3 days"
6. SEARCH      — Full-text search across titles, narratives, and knowledge docs
7. SYNC        — Bidirectional GitHub + Notion integration

How It Compares

Quick Start

MCP Server (recommended)

pip install focalpoint

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "fpms": {
      "command": "focalpoint"
    }
  }
}

OpenClaw: Search focalpoint-memory in ClawHub and install.

23 tools are now available. Start a conversation and say bootstrap.

Python API

from fpms.spine import SpineEngine

engine = SpineEngine(db_path="./data/fpms.db")

# Cold start
bundle = engine.bootstrap()

# Create work items
engine.execute_tool("create_node", {
    "title": "Ship MVP", "node_type": "project", "is_root": True
})

# Prepare work context
workbench = engine.activate_workbench(node_id, role="execution")
# Returns: goal, knowledge, context, subtasks, suggested_next, role_prompt

# Proactive risk scan
alerts = engine.heartbeat()

Work Mode

Workbench — prepare before you work

You: "Work on the payment system task"
AI calls activate_workbench(node_id, role="execution")
-> Gets: goal, knowledge docs, context bundle, sorted subtasks,
   suggested next step, and execution role prompt
-> AI enters role, reads background, starts working

Three Roles

Three-Province Review

For major decisions: Strategy produces requirements -> Review + Engineer review in parallel -> Both approve or reject -> Max 3 rejections then escalate to human.

Knowledge Documents

Attach design docs to nodes. Child tasks inherit parent knowledge automatically.

project "Payment System"
|-- overview.md        <- Project background
|-- architecture.md    <- Design docs
|
\-- task "Implement API"
    -> Inherits overview + architecture automatically

Available Tools (23)

Architecture

Brain (LLM)              Spine (FocalPoint engine)
  |                         |
  | -- Tool Call -->        | Validate -> Write SQLite -> Narrative -> Audit
  |                         |
  | <-- Context ---         | Assemble L0/L1/L2 -> Trim -> Inject prompt

Brain = LLM. Only reads context and issues Tool Calls. Spine = Deterministic engine. All logic here. LLM never touches storage directly.

Storage: Pure SQLite. No vector DB, no Redis, no PostgreSQL.

Stats

Requirements

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

License

Business Source License 1.1 — Free to use, modify, and deploy. Cannot be used to build a competing commercial memory service. Converts to Apache 2.0 on 2030-03-22.

Links

• PyPI
• ClawHub
• Product Introduction
• Usage Guide
• Work Mode Guide