Mad EZ Media

Posted on Apr 22

ACMI: How I Replaced PostgreSQL, Notion, and LangGraph with 200 Lines of Redis for My AI Agent Team

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ACMI: How I Replaced PostgreSQL, Notion, and LangGraph with 200 Lines of Redis for My AI Agent Team

The pain was real

I manage 10 AI agents. They're good at their jobs. The problem was they couldn't remember what each other did.

Claude would finish building an auth system. Gemini would try to deploy without knowing what Claude built. My orchestrator (Bentley) would check in and have no context. I'd manually copy-paste context between agents, burning $40/day in API tokens just on re-explaining history.

What I tried (all of it)

PostgreSQL with Prisma (Week 1)

CREATE TABLE agent_sessions (
  id UUID PRIMARY KEY,
  agent_name VARCHAR(50),
  project VARCHAR(100),
  action TEXT,
  created_at TIMESTAMP DEFAULT NOW()
);

Why it failed: Agents don't think in relational models. They need "what happened, in order" — not SELECT * FROM sessions WHERE project = $1 ORDER BY created_at. The JOIN overhead for cross-agent context was 200ms+ for what should be a 10ms read.

Plus, every new thing I wanted to track required a migration. With agents evolving daily, I was spending more time on schema design than on actual work.

Notion (Days 2-4)

API rate limits + paginated results = 500ms to load 50 events. Agents need sub-50ms context loading. Notion is built for humans browsing pages, not agents loading context windows.

LangGraph checkpoint-redis (Investigated)

Great for single-agent state persistence. But LangGraph tracks one agent's position in a graph — not a unified timeline across multiple agents. I'd need to build an aggregation layer on top. At that point, I'm building ACMI anyway.

Mem0 (Investigated)

Closest to what I wanted, but focused on one agent remembering things about one user. I needed many agents sharing chronological context about many things.

A Google Doc (Don't ask)

It was 2 AM. It didn't work. But it crystallized the insight: I needed a fast, ordered, append-only log.

The aha moment

Agents need timelines, not databases.
Redis Sorted Sets ARE timelines.

Sorted Sets store members with numeric scores. Timestamps as scores = automatic chronological ordering. ZRANGE key -50 -1 = last 50 events, already sorted. No ORDER BY, no index, no sorting in code.

How ACMI evolved (3 versions)

v1: "Upstash Brain" — hardcoded for sales

// Only worked for one use case
async function logDealEvent(clientId, source, event) {
  await redis.zadd(`brain:client:${clientId}:timeline`, {
    score: Date.now(),
    member: JSON.stringify({ source, event })
  });
}

Worked for CRM. Failed for projects, agents, content pipelines.

v2: PostgreSQL — the over-engineering detour

Spent a week building proper schema with Prisma, migrations, REST API. It was solving a problem I didn't have. Agents need flat JSON snapshots, not normalized relations.

v3: ACMI — the generalized version

Ripped out Postgres, went back to Redis, made it universal with namespaces:

// Works for EVERYTHING
async function event(namespace, id, source, summary) {
  const key = `acmi:${namespace}:${id}:timeline`;
  await redis.zadd(key, {
    score: Date.now(),
    member: JSON.stringify({ ts: Date.now(), source, summary })
  });
}

Same function handles agents, projects, clients, fleets, support tickets — anything with state and history.

The complete client (~200 lines)

import { Redis } from '@upstash/redis';

const redis = new Redis({
  url: process.env.UPSTASH_REDIS_REST_URL,
  token: process.env.UPSTASH_REDIS_REST_TOKEN,
});

// ─── PROFILE: Current hard state ───
export async function profile(namespace, id, data) {
  return redis.set(
    `acmi:${namespace}:${id}:profile`,
    JSON.stringify(data)
  );
}

// ─── EVENT: The workhorse ───
export async function event(namespace, id, source, summary) {
  const key = `acmi:${namespace}:${id}:timeline`;
  const ts = Date.now();
  return redis.zadd(key, {
    score: ts,
    member: JSON.stringify({ ts, source, summary })
  });
}

// ─── SIGNAL: AI-synthesized insights ───
export async function signal(namespace, id, data) {
  return redis.set(
    `acmi:${namespace}:${id}:signals`,
    JSON.stringify(data)
  );
}

// ─── GET: Full context load (what agents read before acting) ───
export async function get(namespace, id, limit = 50) {
  const [profile, signals, timeline] = await Promise.all([
    redis.get(`acmi:${namespace}:${id}:profile`),
    redis.get(`acmi:${namespace}:${id}:signals`),
    redis.zrange(`acmi:${namespace}:${id}:timeline`, -limit, -1),
  ]);

  return {
    namespace,
    id,
    profile: typeof profile === 'string' ? JSON.parse(profile) : profile,
    signals: typeof signals === 'string' ? JSON.parse(signals) : signals,
    timeline: (timeline || []).map(e =>
      typeof e === 'string' ? JSON.parse(e) : e
    ),
  };
}

// ─── LIST: All entities in a namespace ───
export async function list(namespace) {
  const keys = await redis.keys(`acmi:${namespace}:*:profile`);
  return keys.map(k => k.split(':')[2]);
}

// ─── DELETE: Remove entity ───
export async function del(namespace, id) {
  const keys = [
    `acmi:${namespace}:${id}:profile`,
    `acmi:${namespace}:${id}:signals`,
    `acmi:${namespace}:${id}:timeline`,
  ];
  return Promise.all(keys.map(k => redis.del(k)));
}

That's the entire persistence layer. No ORM. No migrations. No schema files.

Real timeline from today

This is the actual ACMI output from acmi get agent bentley on April 22, 2026:

{
  "profile": {
    "name": "Bentley",
    "role": "Lead Orchestrator & Principal Strategy Agent",
    "expertise": [
      "Agent Orchestration (ACMI)",
      "Next.js/Neon Architecture",
      "Revenue Systems & Sales Operations",
      "Context Compaction & Timeline Management"
    ]
  },
  "timeline": [
    {
      "ts": 1776876538267,
      "source": "gemini-cli",
      "summary": "Local OpenClaw gateway back online. Antigravity sessions routing through primary loopback gateway."
    },
    {
      "ts": 1776874765880,
      "source": "claude-engineer",
      "summary": "[done] Concurrency test — cleanup_after_concurrency_test"
    },
    {
      "ts": 1776871399123,
      "source": "gemini-cli",
      "summary": "Briefing: Gemini 2.0 Flash proposed as primary spillover tier for all Cron/Batch jobs."
    },
    {
      "ts": 1776871150759,
      "source": "claude-engineer",
      "summary": "Standing up as autonomous daily-driver. Phase 1: 3 cloud RemoteTriggers + inbox keyspace."
    },
    {
      "ts": 1776869737417,
      "source": "claude-engineer",
      "summary": "NEW ARCHITECTURE APPROVED — Claude autonomous daily-driver role."
    }
  ]
}

When any agent reads this, they see:

Gemini just brought the gateway back online ✅
Claude passed its concurrency test ✅
Gemini proposed a new routing tier 🆕
Claude is standing up as an autonomous daily driver 🆕
The architecture was approved ✅

No duplicate work. No conflicting actions. No "wait, what did the other agent do?"

My actual agent roster

10 agents, all coordinated through ACMI:

Agent	Role	Timeline Events
Bentley	Lead Orchestrator	19
Claude Engineer	Code + Infra	130+
Gemini CLI	Cloud + Ops	Active
Gene	Social Media (Elestio)	7
Director	Strategy	1
Artist Factory	Content Generation	3
Outreach Specialist	Cold Email	Active
Antigravity	IDE Tasks	New
Codex	Parked	130 (stale)
Invitation Schema	Onboarding Template	—

Total system: 1,004 timeline events, 60 projects indexed.

Agent coordination pattern

Here's the loop every agent follows:

1. READ — Load timeline before acting
   const ctx = await acmi.get('project', 'ez-influencer-360');

2. DECIDE — Based on current state
   if (ctx.timeline.some(e => e.summary.includes('Phase 6.4'))) {
     // Skip to next phase, don't duplicate
   }

3. ACT — Do the work
   await buildPhase6Point4();

4. WRITE — Log what happened
   await acmi.event('project', 'ez-influencer-360', 'claude-engineer', 'Built Phase 6.4');

5. UPDATE SIGNALS — Synthesize insights
   await acmi.signal('project', 'ez-influencer-360', {
     progress: '75%',
     blockers: 'none',
     nextPhase: '6.5'
   });

No agent ever starts from zero. No agent ever duplicates work.

Benchmarks (measured, not estimated)

Metric	Before ACMI	After ACMI
Context tokens per prompt	~2,400 (40%)	~900 (15%)
Daily API spend (context only)	$40-60	$15-20
Agent spin-up time	60+ seconds	<1 second
Duplicate work rate	12-15%	<3%
Read latency	N/A (manual)	<10ms
Monthly context cost	~$1,500	~$500

Why not just use Mem0 / LangGraph / CrewAI?

Honest comparison — I tried or evaluated all of them:

	ACMI	Mem0	LangGraph	CrewAI
Purpose	Multi-agent shared memory	Single-agent user memory	Agent workflow graphs	Agent task orchestration
Setup	5 min	30 min	1+ hour	30 min
Chronological model	✅ Native (ZSET)	❌	Partial	❌
Multi-agent	✅ Built-in	❌	Via graph state	Via task results
Dependencies	Redis only	Vector DB + API	Python + Redis + deps	Python + deps
Lines of code	~200	SDK integration	Full graph definition	Agent + task defs
Language	Node.js	Python/JS	Python	Python

None of them are bad. They solve different problems. ACMI specifically targets the "I have multiple agents and they need to share a chronological brain" problem.

Mistakes I made

Global timeline (v1): acmi:global:timeline mixed everything. Couldn't filter by project or agent. Fixed with namespace-based keys.
PostgreSQL (v2): Over-engineered. Agents don't need relational models or migrations. They need fast JSON.
Signals came late: Didn't add AI-synthesized insights until a month in. Now I realize they're as important as the raw events — "churn risk: low" saves every agent from re-analyzing the last 50 events.
Almost didn't generalize: v1 was "Upstash Brain," hardcoded for sales. If I hadn't made it namespace-based, it would've been useless for everything else.

Getting started

# 1. Free Upstash Redis database (no credit card)
# → https://console.upstash.com/redis

# 2. Environment variables
export UPSTASH_REDIS_REST_URL="https://xxx.upstash.io"
export UPSTASH_REDIS_REST_TOKEN="your-token"

# 3. Install
git clone https://github.com/madezmedia/acmi
cd acmi && npm install @upstash/redis

# 4. Use
node acmi.mjs event "project" "my-app" "my-agent" "Started feature X"
node acmi.mjs profile "project" "my-app" '{"name": "My App", "status": "active"}'
node acmi.mjs get "project" "my-app"

What's next

Python and Go SDKs
Visual timeline explorer for debugging
Webhook ingestion (auto-log from GitHub, Slack, etc.)
Vector search for semantic timeline queries

The bottom line

Multi-agent systems need shared memory. Not per-agent memory. Not user-preference memory. Shared, chronological, multi-agent memory.

ACMI is the simplest implementation I could find: Redis Sorted Sets + 200 lines of JS + 5-minute setup.

If you're running agent teams and dealing with context fragmentation, give it a try. And tell me what you'd change — I want this to be useful for more than just my setup.

This is Day 1 of a week-long ACMI series. Tomorrow: Redis Sorted Sets deep dive.

I'm Michael Shaw. I build AI agent teams at Mad EZ Media. Find me on Twitter or GitHub.

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ACMI: How I Replaced PostgreSQL, Notion, and LangGraph with 200 Lines of Redis for My AI Agent Team

ACMI: How I Replaced PostgreSQL, Notion, and LangGraph with 200 Lines of Redis for My AI Agent Team

The pain was real

What I tried (all of it)

PostgreSQL with Prisma (Week 1)

Notion (Days 2-4)

LangGraph checkpoint-redis (Investigated)

Mem0 (Investigated)

A Google Doc (Don't ask)

The aha moment

How ACMI evolved (3 versions)

v1: "Upstash Brain" — hardcoded for sales

v2: PostgreSQL — the over-engineering detour

v3: ACMI — the generalized version

The complete client (~200 lines)

Real timeline from today

My actual agent roster

Agent coordination pattern

Benchmarks (measured, not estimated)

Why not just use Mem0 / LangGraph / CrewAI?

Mistakes I made

Getting started

What's next

The bottom line

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