Memory for Agents

Your Agent Wakes Up Stupid Every Day

Engram gives AI agents persistent, queryable memory. Three API calls. Full context across every session. Your agent finally remembers.

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agent.py
from engram import Memory

# Your agent gets memory in one line
mem = Memory("api_key_here")

# Remember anything
mem.remember("User prefers concise replies")
mem.remember("Project deadline is March 14")

# Recall what matters
context = mem.recall("user communication style")
# → "User prefers concise replies" (0.94)

# Forget when asked
mem.forget("outdated project info")
The Problem

Context Amnesia Costs Real Money

Every time your agent restarts, it loses everything. Past decisions, user preferences, research, relationships. You're paying for the same work twice. Or ten times.

67%
of agent token spend is re-establishing context that was already known
0
major agent frameworks ship with persistent memory built in
3
API calls to give your agent a brain that persists across sessions
Without Engram
User: "Remember, I hate long emails"
Agent: "Got it!"

[session restart]

User: "Draft an email to the team"
Agent: *writes 500-word essay*
User: "I TOLD you I hate long emails"
Agent: "Sorry! I'll keep it brief."

[session restart]

User: "Draft an email to the team"
Agent: *writes 500-word essay again*
With Engram
User: "Remember, I hate long emails"
Agent: mem.remember("User hates long emails,
  prefers concise communication")

[session restart]

User: "Draft an email to the team"
Agent: ctx = mem.recall("email preferences")
→ "User hates long emails"

Agent: *writes 3 tight sentences*
User: "Perfect."

[every session, forever]
Data Sovereignty

Your Memory. Your Infrastructure. Your Keys.

Engram provides the architecture. You own every byte. We never see, read, or train on your agent's memories.

Encrypted & Isolated

Every agent gets its own isolated namespace. Memories are encrypted at rest and in transit. No shared infrastructure between tenants.

Export Anytime

Full data export in standard formats whenever you want. No lock-in. Your memories are portable across any infrastructure.

Bring Your Own Storage

Pro and Fleet plans support your own S3, Supabase, or local disk as the storage backend. Your data never touches our servers.

Zero Training on Your Data

We will never use your agent's memories to train models or improve our service. Your competitive advantage stays yours.

Who It's For

Two Problems. One API.

Instant Setup
You have an agent. It works. But every
session it forgets everything.

You don't want to design a memory
architecture. You don't want to manage
a vector database. You don't want to
write chunking logic.

You want to add one import and move on.

pip install engram → done.
Your agent remembers. Ship it.
Multi-Agent & Swarms
You have 5 agents. They each learn
things the others need to know.

Agent A discovers the user hates
verbose output. Agent B writes a
500-word status update anyway.

Engram gives each agent its own
namespace with optional shared layers.
Private thoughts + collective memory.

Swarm intelligence without the chaos.
Not Just a Vector Store

Four Types of Memory, One API

Different memories need different retrieval. Engram handles the taxonomy so you don't have to.

Episodic

What happened. Conversation logs, task completions, decisions made. Time-aware retrieval so "what did we decide last Tuesday" just works.

Semantic

What you know. Ingested articles, research, domain knowledge. The stuff your agent learned from reading, not from doing.

Procedural

How to do things. Learned workflows, tool preferences, patterns that worked. Your agent builds muscle memory.

Relational

Who you know. People, preferences, relationship history. Query by person, not by keyword.

How It Works

Three Methods. That's It.

No config files. No schema design. No vector DB management. Install, authenticate, remember.

1

Install

One command. Works with Python, Node, or plain HTTP. pip install engram

2

Remember

Call mem.remember(text) with anything worth keeping. Engram chunks it, embeds it, classifies the memory type, and stores it. You don't think about any of that.

3

Recall

Call mem.recall(query) and get back the most relevant memories with source and confidence scores. Filtered by memory type, time range, or person automatically.

Pricing

Pay for What You Remember

Start free. Scale when your agent's memory grows. Join the waitlist now and lock in 50% off at launch.

Starter
For personal agents
$0
forever
  • 1,000 memories
  • 10,000 recalls/month
  • 1 agent namespace
  • All 4 memory types
  • 7-day memory retention
Join Waitlist
Pro
For serious agent operators
$19 $9.50
/month — early bird pricing
  • 100,000 memories
  • Unlimited recalls
  • 10 agent namespaces
  • All 4 memory types
  • Unlimited retention
  • URL ingestion
  • Priority support
Join Waitlist — 50% Off
Fleet
For agent teams & platforms
$99 $49.50
/month — early bird pricing
  • 1M memories
  • Unlimited everything
  • Unlimited namespaces
  • Shared memory layers
  • Memory analytics
  • SSO & team management
  • SLA & dedicated support
Join Waitlist — 50% Off
FAQ

Questions

Why not just use ChromaDB / Pinecone / Weaviate myself?
You can. Engram isn't a vector database. It's a memory system built on top of one. We handle chunking strategy, memory type classification, time-aware retrieval, and relevance tuning specifically for agent use cases. You'd spend weeks building what Engram gives you in one import.
What agent frameworks does it work with?
All of them. OpenClaw, LangChain, CrewAI, AutoGen, custom builds. If your agent can make an HTTP call or import a Python/Node package, it works.
Is my agent's memory private?
Completely. You own your data, full stop. Each agent gets an isolated encrypted namespace. We never read, access, or train on your memories. Pro and Fleet plans can bring their own storage backend — your data never even touches our servers. Export or delete everything at any time. See our data sovereignty commitment →
How fast is recall?
Sub-100ms for most queries. We optimize embedding and retrieval for the kind of queries agents actually make, which are very different from general-purpose search.
Can multiple agents share memory?
Yes, on the Fleet plan. Shared memory layers let agent teams build collective knowledge while maintaining individual namespaces. Think of it as a shared brain with private thoughts.
What happens if I exceed my plan limits?
We don't cut you off. Recalls continue working but new memories queue until you upgrade or the next billing cycle. Your agent never loses access to what it already knows.

Stop Paying for Amnesia

Your agent deserves to remember. Join the waitlist and lock in 50% off at launch.

Join the Waitlist
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