MemClaw Blog · June 2026

Learn to Build Multi-Agent Fleets with MemClaw.
A Hands-On, Six-Part Series.

From “hello fleet” to self-improving, governed memory — one continuous build on a single shared MemClaw stack. No throwaway demos; the fleet’s memory accumulates across every part.

Caura.AI · 4 min read

Most “agent memory” tutorials stop at a single agent remembering a single thing. Real fleets are messier: many agents, shared knowledge, permissions that actually hold, and a store that has to stay trustworthy as it grows. So we wrote the series we wished existed.

It’s a hands-on, six-part path that builds a working multi-agent fleet on one shared, governed MemClaw memory — starting from nothing and ending with a self-improving, self-cleaning knowledge layer. Every part runs against the same self-hosted stack, so the memory you create in Part 1 is still there, sharper, by Part 5.

You don’t read this series. You build alongside it — one stack, six steps, a fleet that remembers more each time.

The series

Work through them in order — each part builds on the stack the last one left running:

1

Building a Multi-Agent Fleet with MemClaw and Claude Code

Spin up MemClaw, connect Claude Code over MCP, give each agent its own identity, and watch knowledge flow between them.

You’ll learn: Give three agents one shared, governed brain in about 30 minutes — with zero custom code.

Start Part 1 →
2

The Memory Dashboard: a browsable window into your fleet's mind

A browsable window into your fleet's memory: search, graph, audit, write, and govern — from one HTML file and a reverse proxy.

You’ll learn: See, search, and govern shared memory without leaving the browser.

Start Part 2 →
3

Governed memory: scopes, trust tiers & keystone policies

Who sees what, who can change the fleet's knowledge, and the keystone policies every agent must obey.

You’ll learn: Visibility scopes, trust tiers, and deterministic keystone policy — enforcement, not hope.

Start Part 3 →
4

The Karpathy Loop: memory that learns from outcomes

Report outcomes; memory reinforces what works and writes rules from what fails — and each agent tunes its own recall.

You’ll learn: Turn a static store into a fleet that gets sharper every interaction.

Start Part 4 →
5

Memory hygiene at scale: contradictions, supersession & the crystallizer

Automatic contradiction detection and supersession, the 8-state lifecycle, the crystallizer hygiene scan, and memclaw_insights.

You’ll learn: What keeps a fleet's shared memory from rotting as it grows.

Start Part 5 →
6

The knowledge graph: entities, relations & graph-boosted recall

Coming soon

On every write, MemClaw quietly builds a graph of your domain — entities and the relations between them — and uses it to make recall smarter.

You’ll learn: Entity extraction, typed relations, and graph-boosted hybrid search.

New here? Start at the series index and follow the parts top to bottom.

What you’ll walk away with

  • →A running fleet — multiple agents, distinct identities, sharing one governed memory over MCP.
  • →Governance you can prove — scopes and keystone policies enforced in the storage layer, not asked for in a prompt.
  • →Memory that improves — outcomes reinforce what works, failures mint preventive rules, recall tunes per agent.
  • →Memory that stays clean — automatic contradiction detection, supersession, and a crystallizer that merges near-duplicates with provenance intact.

It’s all open source — the storage layer, the 12 MCP tools, the lifecycle engine, the audit trail, Apache 2.0. Clone it, run it on your own Postgres + pgvector + Neo4j, and read every line of the boundary you’re trusting.

Six parts. One stack. A fleet that compounds.

Start building — Part 1 takes about 30 minutes.

Start Part 1 →★ OSS on GitHub →

MemClaw is governed shared memory for AI agent fleets — multi-agent, multi-fleet, multi-tenant, with permissions and audit trails. Built by Caura.ai.

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