The problem nobody talks about
When you run multiple AI agents, each one starts completely fresh.
Zero knowledge of what other agents learned, decided, or remembered.
Agent A spends an hour learning your codebase structure.
Agent B starts tomorrow knowing nothing.
That's not a system — that's busywork.
Why naive solutions fail
Dumping everything in the prompt:
Token cost explodes. At 10 agents running 50 sessions each,
you're burning thousands of tokens on context that could be
retrieved on demand.
Shared vector DB:
Works, but every agent needs custom retrieval logic. You spend
more time on the memory layer than on the actual agents.
No shared state:
Agents contradict each other. Agent A decides X. Agent B decides
not-X. Nobody catches it.
What actually works
A dedicated memory layer with four properties:
Semantic retrieval — not keyword search. recall("database
schema") returns memories about your Supabase tables even if stored
as "our Postgres setup uses UUID primary keys."Conflict detection — when Agent B stores something that
contradicts Agent A, flag it. Human resolves. No silent
contradictions.Namespaces — separate memory pools per project or client.
Agent stack for Client A never bleeds into Client B.Team sharing — your whole engineering team shares one memory
pool. One agent learns something, everyone benefits.
How I built this
Built AgentMemo as an MCP server — four tools:
remember() — stores a memory with embedding
recall() — semantic search across all memories
forget() — removes a memory by ID
list_conflicts() — shows contradictions between stored memories
Install in 60 seconds:
npm install -g @pulsoai/agentmemo-mcp
Add to Claude Desktop config and every conversation becomes a
persistent memory session shared across your entire agent stack.
Pricing
Free tier: 50 memories · 20 recalls/month · 1 user
Starter $29/mo: 2,000 memories · 500 recalls · 1 user
Team $99/mo: unlimited memories · unlimited recalls ·
10 users · unlimited namespaces
agentmemo.vercel.app
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