Deploying teams of agents requires manually building inter-agent messaging, shared memory, and orchestration from scratch; no standardized framework primitives exist for these concerns. This creates significant setup friction and makes multi-agent coordination brittle and non-portable. Additionally, agents with differing utility functions have no principled consensus mechanism for shared facts or coordination points.

Developers building multi-agent systems waste weeks hand-rolling inter-agent messaging, shared memory, and consensus logic that breaks every time they swap frameworks or add new agents.

AI engineers at startups and enterprises deploying multi-agent workflows (e.g., on CrewAI, AutoGen, LangGraph) who need agents to reliably coordinate across tasks.

Multi-agent deployments are exploding but every team reinvents brittle glue code; a drop-in SDK that provides typed message passing, shared state, and conflict resolution would save weeks per project and teams already pay for orchestration tools like LangSmith and modal infrastructure.

Ship a Python/TS SDK with three primitives — a typed pub/sub message bus, a conflict-free replicated shared memory store (CRDT-based), and a pluggable consensus module — with adapters for CrewAI, AutoGen, and LangGraph as the MVP scope.

The AI developer tooling market is projected at $30B+ by 2028; the multi-agent infrastructure slice serving tens of thousands of teams building agent systems today is conservatively a $500M+ opportunity.