Mission Control
github.com/builderz-labs/mission-control- Category
- AI Agents
- Rank
- No. 288Tools index
Previous survey · No. 300 ·
- Pricing
- Open Source
- Type
- APP
- Use case
- Agent Building · Deployment & Operations
- Interfaces
- Web · CLI · API
- Builder
- @builderz-labs
- GitHub
- 6.3k stars
- Latest release
- v2.3.0
- Date
About
An open-source dashboard for AI agent orchestration that helps you manage agent fleets, track tasks, monitor costs, and orchestrate workflows from a single interface. Features real-time monitoring, role-based access control, and integrations with multiple AI agent frameworks.
What it does
Mission Control wraps existing agent runtimes with an operator layer. Work enters a task lifecycle, agents claim or receive assignments, results pass through review, and SQLite records state locally. Operators can connect through a browser, command line, REST, MCP, WebSocket, or server-sent events.
Why it's ranked here
The project covers more than fleet visibility. It defines task claiming, capacity limits, recurring work, stale-task recovery, approval records, and completion receipts. Its broad interface surface and documented gateway-free loop make it practical to trial. Alpha status and uneven adapter depth keep it from being a low-risk default.
What's good
The task model captures ownership, priority, execution, review, failure, and verified completion instead of treating logs as proof of success. Agents can self-register, claim work atomically, report heartbeats, and receive pending items. Security measures include host filtering, origin checks, content security policy headers, session authentication, API keys, and role checks.
Tradeoffs
Mission Control requires self-hosting and local operational care. SQLite and native dependencies complicate cross-platform standalone builds, which must be built on the target operating system and architecture. Live session messaging requires a connected runtime gateway, adapter depth varies, and alpha APIs, schemas, and configuration may change. Deployment documentation also states an older Node prerequisite than the package requirement.
How to use it well
Use it when several agents or runtimes make task ownership, failures, review status, and accumulated cost difficult to reconstruct. Start with the REST queue and heartbeat loop, then add schedules, review gates, or a gateway as needed. Skip it for one understandable local agent, managed multi-tenant hosting, or defining an agent's reasoning and tool loop.
Technical notes+
README.md describes a Next.js control plane backed by SQLite in WAL mode and exposes REST/OpenAPI, MCP, CLI, WebSocket, and SSE boundaries. package.json requires Node.js 22 or newer, pnpm, Next.js, React, TypeScript, better-sqlite3, Zod, Zustand, Vitest, and Playwright. src/store/index.ts defines client state for tasks, agents, sessions, costs, approvals, tenants, projects, chat, and connectivity, while src/index.ts contains an older overlapping store shape. src/lib/adapters/index.ts registers OpenClaw, generic, CrewAI, LangGraph, AutoGen, and Claude SDK adapters. src/proxy.ts applies host checks, origin validation, session and API-key gates, CSP, frame denial, and content-type protections; src/proxy.test.ts covers host filtering, health access, and API-key shape handling. docs/deployment.md documents direct, standalone, and Docker deployment, including native binary portability constraints.
Observed
- License
- MIT
- Primary language
- TypeScript
- Source install
- Node.js 22 or newer with pnpm
- Packaging
- Source install, Docker Compose, published multi-architecture container image, and standalone Next.js build
- Interfaces
- Web UI, CLI, MCP server, OpenAPI-described REST API, WebSocket, and SSE
- State storage
- Local SQLite through better-sqlite3 with WAL mode
- Runtime adapters
- OpenClaw, generic, CrewAI, LangGraph, AutoGen, and Claude SDK
- Verification stack
- Vitest, Playwright, ESLint, TypeScript checks, builds, and API contract parity checks
Read from README.md, package.json, src/index.ts, src/proxy.ts, src/proxy.test.ts, src/store/index.ts, src/types/index.ts, src/lib/adapters/index.ts, docs/deployment.md, docs/quickstart.md, docs/agent-setup.md, docs/orchestration.md.
What it can do
Monitor AI agent fleet status
Connected AI agents → Real-time dashboard showing agent health and activity
Track task execution across agents
Agent task assignments and workflows → Task status updates and completion reports
Monitor AI usage costs
Agent API calls and resource consumption → Cost tracking data and usage analytics
Orchestrate multi-agent workflows
Workflow definitions and agent assignments → Coordinated task execution across agent fleet
Manage user access permissions
User roles and permission settings → Role-based access control enforcement
Integrate with AI agent frameworks
OpenClaw and Claude Code agent configurations → Unified management interface for multiple frameworks
Display comprehensive monitoring panels
Agent performance and system metrics → 32 specialized monitoring panels with real-time data
Intel on Mission Control
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