FocalPoint — AI Cognitive Operating System

Your AI forgets everything between conversations. FocalPoint fixes that.

Not just memory — cognitive infrastructure. FocalPoint tracks your projects, prepares context before tasks, uses a Three-Province review system for quality decisions, and proactively alerts you about stuck work.

The Problem

AI agents are stateless. Every conversation starts from zero — no memory of your projects, decisions, or progress. You waste time re-explaining context and manually tracking what's stuck.

Existing solutions only go halfway:

They remember what was said. FocalPoint manages what needs to be done.

What You Get

• Structured memory — Goal > Project > Milestone > Task hierarchy with status lifecycle
• Workbench — One call prepares goal, knowledge, context, subtasks, and role prompt
• Proactive alerts — Heartbeat detects blocked, stale, and at-risk tasks automatically
• Knowledge documents — Attach design docs to nodes; child tasks inherit parent knowledge
• Role-based thinking — Strategy, Review, and Execution roles see filtered perspectives
• Three-Province review — Parallel review by two reviewers before execution; max 3 rejections then escalate
• Full-text search — FTS5 search across titles, narratives, and knowledge documents
• GitHub + Notion sync — Issues and pages auto-sync as FocalPoint nodes
• Zero dependencies — Runs 100% locally on SQLite. No vector DB, no Redis, no cloud.

Competitive Comparison

No competitor combines structured task management + proactive alerts + knowledge inheritance + role-based context + review workflow in a single MCP-native package.

Setup

1. Install

pip install focalpoint

2. Add MCP server

# openclaw.yaml
mcp_servers:
  fpms:
    command: focalpoint

3. Restart OpenClaw

That's it. 22 tools are now available.

How It Works

Architecture: Brain-Spine Model

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

SQLite           <- Single source of truth
events.jsonl     <- Audit trail
narratives/*.md  <- Append-only logs
knowledge/{id}/  <- Design documents

No vector database. No Redis. No PostgreSQL. One SQLite file is everything.

Work Mode

Workbench — prepare before you work

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

Three Roles

Same data, different thinking modes. The role prompt guides the AI's perspective.

Three-Province Review — quality decisions

For major decisions (new features, architecture changes, tech choices):

Strategy produces requirements
    |
Review + Engineer review in parallel
|-- Review: checks risks, historical lessons -> approve/reject
|-- Engineer: evaluates feasibility -> approve/reject
    |
Both approve -> proceed to execution
Either rejects -> revise and resubmit
    |
> 3 rejections -> escalate to human

Knowledge Documents — persistent design context

You: "Save this architecture doc to the project"
AI calls set_knowledge(project_id, "architecture", content)
-> Child tasks inherit parent knowledge automatically
-> AI reads project overview without you re-explaining

Types: overview | requirements | architecture | custom names

Log Categories

append_log(node_id, content, category="decision")   # Decision records
append_log(node_id, content, category="feedback")    # User/market feedback
append_log(node_id, content, category="risk")        # Risks and lessons
append_log(node_id, content, category="technical")   # Technical details
append_log(node_id, content, category="progress")    # Progress updates
append_log(node_id, content, category="general")     # Default

Different roles see different categories. Strategy sees decisions + feedback. Execution sees technical + progress.

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"
AI:  append_log(node_id, "Chose Stripe — better API, lower fees", category="decision")
(Two weeks later)
You: "Why did we pick Stripe?"
AI:  Searches decisions -> "You decided on March 15 — better API and lower 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

Full-text search

You: "Find everything related to caching decisions"
AI:  search_nodes(query="caching decisions")
-> Finds nodes by title, narrative content, and knowledge docs

Available Tools (22)

Write (11)

Read (5)

Cognitive (4)

Review (1)

Runtime (1)

Automatic Memory Rules

Follow these rules in EVERY conversation:

1. Conversation start -> Call bootstrap to load memory
2. Before starting a task -> activate_workbench to prepare context
3. User makes a decision -> append_log with category="decision"
4. Risk identified -> append_log with category="risk"
5. Task progresses -> update_status
6. Design conclusions -> set_knowledge to persist for future sessions
7. Before conversation ends -> append_log key takeaways
8. Every ~10 min -> heartbeat to check for risks

Stats

Requirements

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

Links

• GitHub
• PyPI