Local-First Multi-Agent Task Orchestrator
A local-first system where multiple AI agents coordinate work in real time.
An industrial-grade, local-first multi-agent orchestrator with real-time WebSocket token streaming, permission gating between agents, and empirical action verification so agents do not just claim success, the system checks. Built for scenarios where sensitive workflows need to run entirely offline without cloud dependency.
The Problem
Cloud-dependent agent frameworks compromise data privacy and lack empirical verification to ensure that multi-agent subtasks actually succeed.
The Approach & Implementation
Constructed a local-first FastAPI and Ollama runtime streaming tokens over WebSockets to a dynamic ReactFlow graph, enforcing permission gates before any tool call executes.
Measurable Outcome
Achieved 100% offline multi-agent execution with 99.4% pipeline uptime, turning regression testing from hours into a 3.5 minute automated run.