Decision Memory Engine

Python and FastAPI decision-intelligence service for storing prior decisions, rationale, confidence, stale assumptions, and operator context recovery.

What this repo proves

Teams make better follow-on decisions when they can recover the original rationale chain instead of only inheriting the headline outcome.

Why this repo exists

Organizations often remember what was decided but lose the more valuable parts:

• which assumptions made the decision safe
• which sources supported the decision
• when the reasoning should be reviewed again
• what changed after the decision was made

decision-memory-engine turns those decision artifacts into reusable memory records. It helps teams decide whether an old decision is still safe to reuse in a new board, product, risk, or operator context.

Screenshots

Overview

Memory Board

Replay

API Summary

What it includes

• FastAPI service for decision memory and context replay
• sample decisions across revenue, risk, platform, real estate, and education lanes
• rationale, stale-signal, and next-review tracking
• owner-lane and replay surfaces
• HTML proof scenes and PNG screenshots generated from the repo
• tests, docs, changelog, and CI

Local run

cd decision-memory-engine
py -3.11 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
.\.venv\Scripts\python.exe -m app.main

Open:

• http://127.0.0.1:5006/
• http://127.0.0.1:5006/memory-board
• http://127.0.0.1:5006/replay
• http://127.0.0.1:5006/owners
• http://127.0.0.1:5006/docs

If that port is occupied:

$env:PORT = "5010"
.\.venv\Scripts\python.exe -m app.main

Validation

cd decision-memory-engine
.\.venv\Scripts\python.exe -m unittest discover -s tests
.\.venv\Scripts\python.exe scripts\run_demo.py
.\.venv\Scripts\python.exe scripts\smoke_check.py
.\.venv\Scripts\python.exe scripts\render_readme_assets.py

API routes

• GET /api/dashboard/summary
• GET /api/decisions
• GET /api/decisions/{decision_id}
• GET /api/timeline
• GET /api/owners
• GET /api/sample
• POST /api/analyze/recollection

Architecture

flowchart LR
  A["Decision records"] --> B["Decision memory service"]
  B --> C["Owner lanes"]
  B --> D["Replay and recollection scoring"]
  B --> E["Operator HTML surfaces"]
  D --> F["Briefing-safe reuse decisions"]

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More detail lives in docs/architecture.md.