mission-control and openclaw-command-center

These are competitors: both provide centralized dashboards for monitoring and controlling multiple AI agents, with overlapping core functionality for task dispatch and workflow orchestration, though Mission Control appears more mature and feature-complete for production enterprise use cases.

mission-control
70
Verified
openclaw-command-center
54
Established
Maintenance 25/25
Adoption 10/25
Maturity 11/25
Community 24/25
Maintenance 13/25
Adoption 10/25
Maturity 11/25
Community 20/25
Stars: 3,502
Forks: 599
Downloads:
Commits (30d): 148
Language: TypeScript
License: MIT
Stars: 136
Forks: 26
Downloads:
Commits (30d): 0
Language: JavaScript
License: MIT
No Package No Dependents
No Package No Dependents

About mission-control

builderz-labs/mission-control

Self-hosted AI agent orchestration platform: dispatch tasks, run multi-agent workflows, monitor spend, and govern operations from one mission control dashboard.

Built on Next.js 16 with TypeScript, it uses SQLite for persistence and WebSocket/SSE for real-time updates—no external databases required. Supports multi-gateway connections with framework adapters for OpenClaw, CrewAI, LangGraph, and AutoGen, plus a Skills Hub for discovering and auditing agent capabilities from public registries. Features a four-layer security evaluation framework with trust scoring, secret detection, and MCP call auditing across configurable hook profiles (minimal/standard/strict).

About openclaw-command-center

jontsai/openclaw-command-center

🤖 AI assistant command and control dashboard — Spawn more Overlords!

Provides real-time monitoring and cost analysis for OpenClaw AI agents with a lightweight, zero-dependency UI built on vanilla JavaScript and Server-Sent Events (SSE) streaming. Supports multiple authentication modes (token, Tailscale, Cloudflare, IP allowlist) and auto-detects OpenClaw workspaces without configuration. Integrates with OpenClaw's session management, Slack threading, and Cerebro topic tracking to deliver unified visibility into active sessions, LLM token usage, system vitals, scheduled tasks, and per-model cost breakdowns.

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