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Persistent Engineering Cognition

Your agentforgets everything.Mnemo doesn't.

Every session starts where the last one ended — architecture decisions, patterns, and context preserved forever.

Every sessionstarts fromzero

You explain your architecture. Again. Your patterns. Again. Every new AI session is a blank slate — weeks of context, gone.

Context lost every single session
session_history
useAuth hook patternlost
DB schema decisionslost
API rate limiting approachlost
Error boundary strategylost
Caching layer designlost
context: null// new session starts here

Memory thatpersists

Mnemo builds a living knowledge graph of your engineering context. Every decision, pattern, and architectural choice is preserved — growing stronger with each session.

  • Semantic connections between concepts
  • Natural decay of stale information
  • Instant recall at session start
ArchitecturePatternsMnemoDecisionsContextHistoryErrors

Four stages ofpersistent cognition

From initialization to evolution — a continuous cycle of learning and recall.

mnemo init

Initialize

Seed the knowledge graph with your project's architecture and patterns.

recall

Recall

Agent pulls relevant context automatically at session start.

during session

Learn

Passive learning — new decisions and patterns are captured as you work.

between sessions

Evolve

Stale info decays naturally. Frequently used knowledge is reinforced.

One memory. Every agent.

Mnemo works with every AI coding agent you use — seamlessly sharing context across all of them.

M
Kiro
Claude Code
Cursor
Copilot
Amazon Q

Get started in seconds

One command. Zero configuration.

$ pip install mnemo-dev
1

Install

Choose your preferred package manager above.

2

Init

Run mnemo init in your project root.

3

Code

Start coding — Mnemo handles the rest.

Built for the wayyou work

Every feature designed to make AI coding sessions feel continuous, not fragmented.

Semantic Search

Find relevant context by meaning, not just keywords. Natural language queries across your entire knowledge base.

Knowledge Graph

Interconnected nodes of engineering context — architecture, patterns, decisions — all linked semantically.

Natural Decay

Stale information fades naturally. Frequently accessed knowledge is reinforced and stays fresh.

Multi-Agent

Works with Kiro, Claude Code, Cursor, Copilot, and Amazon Q. One memory shared across all agents.

Cross-Session

Context persists indefinitely across sessions. Pick up exactly where you left off, every time.

Zero Config

One command to install, one command to init. No configuration files, no setup wizards, no friction.

···

fewer repeated explanations

···

context lost between sessions

···

agents supported

What'sunder the hood

A lightweight layer between your agent and your codebase.

architecture.svg
AI Agent(Kiro, Cursor...)MnemoKnowledge GraphSemantic MemoryMCP ServerCodebaseYour Projectquerycontextindexread

Stop re-explaining your codebase

Give your AI agent the memory it deserves. Install Mnemo and never repeat yourself again.

$ pip install mnemo-dev