FlowBoard v5: The Project Workspace Your AI Agents Actually Run
FlowBoard v5 is a full rebuild of the project workspace that gives your AI agents persistent context across sessions. Instead of re-explaining your project goals, decisions, and task state every time, FlowBoard keeps everything in one place and your agent pulls exactly the slice it needs, lazy-loaded to keep token use low.
What's New in v5
- Rebuilt on React — Every surface (Kanban, Idea Canvas, Overview) now runs on one stack; no vanilla runtime left.
- Event-sourced task store — Every change is an append-only event in SQLite, giving you full history and preventing drift.
- First-class multi-agent support — Stable per-agent identity, self-onboarding for external agents, built-in claim/release/handoff. Your OpenClaw agent, Claude Code, and Cursor all show up live on the same board.
- Ideas to specs — Sketch ideas as connected notes on the canvas; a guided clarify loop turns them into real specs with acceptance criteria, not just task titles.
- Modular Overview — A per-project dashboard composed from widgets: blocked items, approval requests, milestones, CI status, files.
Installation
One-liner install via ClawHub:
openclaw plugins install clawhub:flowboardThen one command brings the dashboard up, and it self-updates from inside the dashboard after that. A Telegram mini app is also available to view the board on your phone.
Who It's For
Developers running multiple AI agents across projects who need a persistent, shared context layer without token waste.
📖 Read the full source: r/openclaw
👀 See Also
OpenGauge: Open-source tool for tracking LLM agent costs locally
OpenGauge is an open-source tool that monitors API calls from LLM agents like OpenClaw, logging token usage, costs, and latency to a local SQLite database. It includes proxy mode for automatic logging, detailed cost statistics, and circuit breaker functionality to prevent runaway loops.
Managing Multiple AI Agent Tasks with Kanban Boards
A developer shares their experience running multiple Claude AI agents in terminal tabs and identifies three key workflow challenges: lack of progress visibility, context loss when switching between tasks, and rate limit interruptions. Their solution involves treating AI tasks like work items on a Kanban board.
Running OpenClaw and Codex CLI Natively on Android via AnyClaw APK
A developer has packaged OpenClaw and Codex CLI into an Android APK called AnyClaw, enabling the gateway and Control UI to run locally on ARM64 Android 7.0+ devices without root. The project required building dependencies from source and patching multiple components to handle Android-specific constraints.
Jake Benchmark v1: Local LLM Performance Testing for OpenClaw AI Agents
A developer tested 7 local LLMs as AI agents with OpenClaw using 22 practical tasks including email processing, meeting scheduling, and phishing detection. Results ranged from 59.4% for Qwen 27B to 1.6% for Nemotron 30B, with detailed conversation logs available.