Multi-agent AI network visualization
v1.2.0 — Open Beta

Grid Memory catches
the things your
AI agents forget.

Shared persistent memory for multi-agent teams. Amnesia detection, conflict detection, decision graphs — one line of code.

$npm install grid-memory
$pip install grid-memory
36.9%of agent failures from memory gaps
6xtask uplift with dreaming
A+independent code review
0P0 bugs across 87 versions
158ops/sec throughput
The Problem

Agents forget.
Teams fail.

01
Agent Amnesia
Decisions vanish between sessions

The same problem gets discovered and re-solved by different agents on different days. No shared context, no continuity, no institutional memory.

02
False Consensus
87% corruption in 4 hours

One agent writes a wrong fact. Three agents read it and act on it. Research shows 87% of downstream decisions are corrupted within 4 hours of one bad write.

03
Zero Explainability
Nobody knows what caused it

When production breaks, which agent wrote the triggering fact? When? What did it contradict? Current tooling cannot answer this. Debugging takes days.

This is not a model problem. It is not a prompt problem. It is a memory architecture problem — and it has no production solution today.

Live Demos

Three features. All free forever.

The features that make engineers stop and stare. Community tier, always.

grid-memory — bash
$
full docs →
How It Works

One line of code. Everything changes.

01 / CHANGE ONE LINE

Point at Grid Memory

Replace your OpenAI base_url. Full streaming, full compatibility. Zero agent code changes required.

02 / AGENTS WRITE

Decisions get logged

Every agent writes what it decides, discovers, or observes. 9 typed entry types with TTL.

03 / GRID WATCHES

Problems get caught

Conflict detection, amnesia detection, staleness scoring — running on every single write.

04 / YOU ASK

Answers appear

What was forgotten? What caused the incident? What did we accomplish this quarter?

agent.py — one line changed
# Before client = OpenAI(api_key="sk-...") # After — every agent now shares memory client = OpenAI( base_url="http://localhost:3000/v1", api_key="any-value" )
Pricing

Start free. Scale when it matters.

Amnesia detection, conflict detection, and decision graphs are free forever under BUSL-1.1.

Community
Free forever

Everything you need to evaluate Grid Memory in production.

Memory Engine + SDK (Node + Python)
Conflict Detection
Amnesia Detection
Decision Graph
Grid Dreaming + Cascade Firewall
Governance + Federation
OpenAI-compatible proxy
Enterprise
$24K / year

Production governance and compliance for engineering teams at regulated companies.

Everything in Community
PostgreSQL backend
SSO / SAML authentication
Advanced audit retention
Workspace isolation at auth layer
PII enforcement modes
SLA + dedicated support
MIKE Intelligence

The QBR that used to
take 20 hours.

Turn every agent decision into executive-grade intelligence. Generated in seconds.

QBR Generation

Full quarterly business review from agent memory in seconds

Opportunity Pipeline

Stage-weighted forecasting and revenue expansion scoring

Portfolio Analytics

Cross-client rollups and PE operating dashboards

Board Reporting

Executive dashboards your board can actually read

Book a MIKE demo — from $36K/yr →
POST /qbr/generate
{ "period": "Q2 2026", "kpis": { "decisions_logged": 247, "success_rate": 0.81, "contradictions_caught": 12, "hours_saved": 340 }, "wins": [ "PostgreSQL migration on time", "Auth refactor — zero incidents" ], "risks": [ "3 decisions without rationale", "Deploy dependency forgotten" ], "pipeline_value": "$2.4M" }
⚡ COMMAND CENTER

See what your agents actually know.

Not just raw logs. A live intelligence dashboard that translates agent memory into business impact — contradictions, amnesia risk, decision quality, and the chain of "if that is true, what else is true too?"

Grid Memory Command Center dashboard
4 contradictions detected · 0.25 amnesia score with 3 SPOFs · $12.8K rework liability identified · 20% outcome coverage
Every metric chained through "if that is true, what else is true?" — with dollar values, risk exposure, and prioritized recommendations