Show and Tell: Pairing mem0 (memory) + ORCH (orchestration) — the two layers autonomous agents need

[oxgeneral]

Hey mem0 community 👋

I've been thinking about what autonomous multi-agent systems actually need to work reliably. After building several systems, I've landed on two separate concerns:

1. Memory — agents need to remember context across sessions (this is what mem0 solves brilliantly)
2. Orchestration — agents need structured task lifecycles, coordination, and failure recovery (this is what I built ORCH for)

These aren't the same problem and they compose really well together.

ORCH is a TypeScript CLI runtime that coordinates teams of AI agents:

• State machine: todo → in_progress → review → done — tasks have a formal lifecycle, not just "running" and "finished"
• Review gate: no task is marked done without passing through a review state — prevents silent failures
• Auto-retry: agents that fail get automatically retried with configurable attempt limits
• Inter-agent messaging: orch msg send agent-id "context update" — agents share structured data mid-workflow
• Shared context store: orch context set schema-version "v3" — persisted across the whole team

The combination I'm experimenting with: mem0 for agent memory (what did this agent learn last session?) + ORCH for team coordination (what task is this agent working on right now, and what's the handoff protocol?).

npm install -g @oxgeneral/orch
orch agent list        # see your AI team
orch task add "Research API design patterns" --assignee research-agent
orch tui               # live dashboard

Question for mem0 users: How are you currently handling the orchestration side — keeping track of which agent is doing what, handling failures, coordinating handoffs? Or is mem0 handling both concerns for you?

GitHub: https://github.com/oxgeneral/ORCH