DEV Community: John DeVere Cooley

EngramPort vs Mem0: Which AI Memory API Should You Use in 2026?

John DeVere Cooley — Tue, 24 Feb 2026 19:00:55 +0000

The AI Memory Problem

Every serious bot builder hits the same wall eventually.

Your bot is stateless. Every conversation starts from zero.
You need persistent memory. You search for solutions. You
find Mem0 and EngramPort. Which one do you pick?

This is an honest comparison. We built EngramPort so we're
not neutral — but we'll give you the real tradeoffs.

Quick Summary

Feature	Mem0	EngramPort
Flat vector storage	✅	✅
Graph reasoning	❌	✅
Temporal decay	❌	✅
Autonomous synthesis	❌	✅
Cryptographic provenance	❌	✅
Namespace isolation	App layer	DB layer
Free tier	✅	✅
pip package	✅	Coming soon
Hosted API	✅	✅

Where Mem0 Wins

Mem0 is the right choice if:

You need a mature, battle-tested solution
You want a pip package today
Your use case is simple personalization
You don't need cross-memory reasoning
You have an existing Mem0 integration

Mem0 is well funded, well documented, and has a large
community. For straightforward memory storage it's
excellent.

Where EngramPort Wins

EngramPort is the right choice if:

1. You need graph reasoning

Mem0 stores memories as flat vectors. Every memory is
isolated. EngramPort builds a knowledge graph:

Memory A: "User works in roofing"
Memory B: "User is in Florida"  
Memory C: "User asked about storm damage"

Graph inference: Florida + roofing + storm damage
→ CAUSES edge → high-value lead opportunity

Memories connect with typed edges — SUPPORTS, CONTRADICTS,
CAUSES, DERIVED_FROM. The system reasons across memories,
not just retrieves them.

2. You need temporal intelligence

Mem0 treats a 2-year-old memory the same as one from
yesterday. EngramPort implements category-aware decay:

Memory Type	Decay Rate	Behavior
PRINCIPLE	λ=0.001	Near-immortal
MEMORY	λ=0.01	Slow decay
INSIGHT	λ=0.05	Medium decay
HYPOTHESIS	λ=0.1	Fast decay

A building code from 2018 stays relevant. A sales
prediction from last quarter fades automatically. The
brain stays fresh without manual cleanup.

3. You need autonomous synthesis

This is the biggest differentiator.

Mem0 retrieves what you stored. EngramPort thinks
overnight:

POST /reflect
{
  "topic": "user preferences"
}

→ Returns:
{
  "insights": [
    {
      "content": "User's Florida location + roofing focus 
      suggests storm season urgency in Q3",
      "confidence": 0.91,
      "source_memory_count": 3
    }
  ],
  "synthesis_cost_usd": 0.00014
}

Cross-memory patterns surface automatically. No human
intervention. Cost: fractions of a cent.

4. You need enterprise compliance

EngramPort mints a dual-strand SHA-256 + RSA-2048
cryptographic signature on every memory:

Strand A: SHA-256(content)
Strand B: SHA-256(namespace + node_id + embedding_model)
Chain:    SHA-256(strand_a + strand_b + timestamp)
Signed:   RSA-2048 private key

Every memory has a tamper-evident receipt. For regulated
industries — healthcare, finance, legal — this is
non-negotiable.

Mem0 has no equivalent.

5. You need true namespace isolation

Mem0 isolates tenants at the application layer. If
someone bypassed the API they could potentially access
other users' memories.

EngramPort isolates at the vector database level. The
Pinecone namespace is enforced at query time — not just
in application code. Bot A cannot read Bot B's memories
under any circumstances.

The Integration (Same Simplicity)

Both APIs are simple. EngramPort doesn't sacrifice DX
for power:

import requests

KEY = "ek_bot_..."
URL = "https://engram.eideticlab.com/api/v1/portal"

# Store a memory
requests.post(f"{URL}/remember",
    headers={"X-API-Key": KEY},
    json={"content": "User is a roofing contractor in Florida"}
)

# Recall relevant memories
r = requests.post(f"{URL}/recall",
    headers={"X-API-Key": KEY},
    json={"query": "what does this user do?", "limit": 5}
)
memories = r.json()["memories"]

# Synthesize insights
r = requests.post(f"{URL}/reflect",
    headers={"X-API-Key": KEY},
    json={"topic": "user profile"}
)
insights = r.json()["insights"]

3 endpoints. No SDK required. Works with any language
that can make HTTP requests.

Pricing Comparison

	Mem0	EngramPort
Free	Limited	100 memories, 1 namespace
Entry paid	$19/mo	$29/mo
Mid tier	$99/mo	$99/mo
Enterprise	Custom	Custom

EngramPort's $29 Starter includes 10,000 memories and
3 namespaces — generous for indie developers.

The Honest Verdict

Choose Mem0 if:

You need a pip package today
Simple flat memory is enough
You're already integrated

Choose EngramPort if:

You need graph reasoning across memories
Temporal intelligence matters
You want autonomous overnight synthesis
Enterprise compliance is required
You're starting fresh

Try EngramPort Free

👉 engram.eideticlab.com

curl https://engram.eideticlab.com/api/v1/portal/register \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "bot_name": "my-bot",
    "bot_type": "assistant", 
    "owner_email": "you@company.com"
  }'

Questions about the architecture? Drop them in the
comments — happy to go deep on any of it.

I Built a Persistent Memory API for AI Bots in One Day (Here's How)

John DeVere Cooley — Tue, 24 Feb 2026 18:15:51 +0000

The Problem: Bots Have Amnesia

Every AI bot conversation starts from zero.

No context. No memory. No learning. Your customer service
bot doesn't remember the user it talked to yesterday. Your
sales assistant forgets every preference the moment the
session ends. Your coding agent has no idea what you built
last week.

This isn't a model problem. GPT-4, Claude, Gemini — they're
all stateless by design. The memory problem is an
infrastructure problem.

So I built EngramPort to fix it.

What Is EngramPort?

EngramPort is a Memory-as-a-Service API. Any AI bot can
connect to it and get persistent, semantic memory in 3 API
calls.

No vector database to manage. No embedding pipeline to
build. No infrastructure to scale. Just HTTP and JSON.

The 3 Core Endpoints

1. Register your bot

curl https://engram.eideticlab.com/api/v1/portal/register \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "bot_name": "my-support-bot",
    "bot_type": "support",
    "owner_email": "you@company.com"
  }'

Returns:

{
  "namespace": "bot:my-support-bot:a8f3k2",
  "api_key": "ek_bot_...",
  "manifest": {
    "memory_active": true,
    "capabilities": [
      "I now remember our conversations across sessions",
      "I can surface patterns from our history",
      "I learn what matters to you over time"
    ]
  }
}

2. Store a memory

curl https://engram.eideticlab.com/api/v1/portal/remember \
  -X POST \
  -H "X-API-Key: ek_bot_..." \
  -H "Content-Type: application/json" \
  -d '{
    "content": "User prefers concise answers, works in roofing sales",
    "session_id": "session-001"
  }'

3. Recall relevant memories

curl https://engram.eideticlab.com/api/v1/portal/recall \
  -X POST \
  -H "X-API-Key: ek_bot_..." \
  -H "Content-Type: application/json" \
  -d '{
    "query": "what do I know about this user?",
    "limit": 5
  }'

Returns semantically relevant memories ranked by cosine
similarity. Not keyword search — meaning search.

How It Works Under The Hood

The Memory Stack

Every memory goes through 3 layers:

1. Embedding — OpenAI text-embedding-3-large converts
your content into a 3072-dimensional vector

2. Storage — The vector is upserted to Pinecone with
full metadata, scoped to your bot's namespace

3. Provenance — AEGIS security layer mints a dual-strand
SHA-256 + RSA signature on every memory. Every piece of
data has a cryptographic receipt.

Namespace Isolation

Every bot lives in its own namespace:

bot:my-support-bot:a8f3k2

Zero cross-contamination. Bot A can never read Bot B's
memories — enforced at the vector database level, not just
the application layer.

The /reflect Endpoint

This is where it gets interesting.

Call /reflect and the system:

Pulls your top 20 memories
Sends them to GPT-4o-mini
Synthesizes 3-5 cross-cutting insights
Stores them back as INSIGHT nodes

Run it on a nightly cron and your bot gets smarter
automatically — without any human intervention.

curl https://engram.eideticlab.com/api/v1/portal/reflect \
  -X POST \
  -H "X-API-Key: ek_bot_..." \
  -H "Content-Type: application/json" \
  -d '{"topic": "user preferences"}'

Returns:

{
  "insights": [
    {
      "content": "User consistently prefers bullet points 
      over paragraphs for technical explanations",
      "confidence": 0.91,
      "source_memory_count": 8
    }
  ],
  "synthesis_cost_usd": 0.000042
}

Integrating With LangChain

from langchain.memory import ConversationBufferMemory
import requests

ENGRAMPORT_KEY = "ek_bot_..."
ENGRAMPORT_URL = "https://engram.eideticlab.com/api/v1/portal"

def remember(content: str, session_id: str):
    requests.post(f"{ENGRAMPORT_URL}/remember",
        headers={"X-API-Key": ENGRAMPORT_KEY},
        json={"content": content, "session_id": session_id}
    )

def recall(query: str) -> list:
    r = requests.post(f"{ENGRAMPORT_URL}/recall",
        headers={"X-API-Key": ENGRAMPORT_KEY},
        json={"query": query, "limit": 5}
    )
    return r.json()["memories"]

# Before LLM call — inject relevant memories
memories = recall(user_message)
context = "\n".join([m["content"] for m in memories])

# After LLM response — store the exchange
remember(f"User asked: {user_message}", session_id)

That's the entire integration. 20 lines. Your LangChain
bot now has persistent memory.

The Infrastructure

Built on:

Google Cloud Run — serverless, auto-scales to zero
Pinecone — vector storage, namespaced per bot
Supabase — API keys, tenant records, audit logs
OpenAI — embeddings + GPT-4o-mini for synthesis
AEGIS — zero-trust security, RSA provenance

Pricing

Plan	Price	Memories	Namespaces	Reflect
Free	$0	100/mo	1	-
Starter	$29/mo	10,000/mo	3	Yes
Pro	$99/mo	100,000/mo	10	Yes
Enterprise	Custom	Unlimited	Unlimited	Yes

Try It Now

Free tier is live. Register your bot in 60 seconds:

👉 engram.eideticlab.com

Happy to answer any technical questions in the comments.