mission-control

Mission Control

Overview

Mission Control is a self-hosted AI agent orchestration platform built with Next.js 16 and TypeScript. It launched in February 2026 and crossed 5,000 GitHub stars by June — a pace that reflects real demand for tools that manage AI agent fleets beyond simple chat interfaces.

The project comes from Builderz Labs, a team that’s been building developer infrastructure in the Web3 and AI space. Their lead maintainer, 0xNyk, has pushed 279 commits across three major releases (v1.0 through v2.0.1) in about four months. That velocity matters for a tool in a space where most competitors are either VC-backed SaaS products with opaque roadmaps or abandoned side projects.

The core problem Mission Control solves is operational chaos. If you’re running multiple AI agents — Claude Code for code review, a CrewAI pipeline for research, a custom LangGraph workflow for data processing — you’re probably stitching together terminal tabs, log files, and spreadsheets to track what’s happening. Mission Control gives you a single pane of glass: 32 dashboard panels covering tasks, agents, skills, logs, tokens, memory, security, cron jobs, alerts, webhooks, and pipelines. Everything runs on SQLite with zero external dependencies, so you can spin it up with pnpm start and be running in minutes.

Why it matters

The AI agent ecosystem is fragmenting fast. Every week brings a new framework — CrewAI, AutoGen, LangGraph, Claude SDK, OpenClaw — and most teams end up running three or four simultaneously. What’s missing isn’t another agent framework. It’s the operational layer: how do you dispatch work to the right agent, track what each one is doing, monitor costs, enforce security policies, and coordinate handoffs between agents with different specializations?

Mission Control fills that gap as a meta-orchestrator. It doesn’t replace your agent frameworks — it sits above them, providing unified task management, cost tracking, and security auditing across all of them. The multi-gateway architecture connects to OpenClaw, CrewAI, LangGraph, AutoGen, and Claude SDK simultaneously through framework adapters. You keep using the tools your team already knows, but you get centralized observability and control.

This connects to a broader trend: as AI agents move from demos to production, the tooling around them needs to mature. We’ve seen this pattern before with containers (Docker → Kubernetes), microservices (individual services → service meshes), and now agents (individual agents → orchestration platforms). Mission Control is one of the first open-source projects attempting that orchestration layer for AI agents, and the 5,300-star traction in four months suggests the timing is right.

Key Features

32-Panel Dashboard. The SPA shell includes panels for tasks, agents, skills, logs, tokens, memory, security, cron jobs, alerts, webhooks, and pipelines. Each panel is a dedicated view with its own data fetching and real-time updates. The Kanban board alone has six columns (inbox → assigned → in progress → review → quality review → done) with drag-and-drop, priority levels, and threaded comments. Most teams won’t use all 32 panels, but having them built-in means you’re not bolting on monitoring tools later.

Zero External Dependencies. The entire stack runs on SQLite (via better-sqlite3 in WAL mode) with no Redis, no Postgres, no Docker required. A single pnpm start boots the whole thing. This is a deliberate architectural choice — it reduces deployment friction for small teams and makes the platform genuinely self-hostable without a DevOps engineer. For production, there’s a hardened Docker Compose profile with read-only filesystems, capability dropping, and HSTS.

Multi-Gateway Agent Orchestration. Framework adapters connect to OpenClaw, CrewAI, LangGraph, AutoGen, and Claude SDK simultaneously. You register specialist agents (researcher, coder, reviewer) and create orchestration rules that route tasks based on agent capabilities. The quality gate system — called Aegis — blocks task completion without human sign-off, which is critical for production workflows where agents can’t just mark their own work as done.

Security Audit and Trust Scoring. Real-time security posture scoring (0-100), secret detection across agent messages, MCP tool call auditing, and injection attempt tracking. Each agent gets a trust score, and operators can configure hook profiles (minimal/standard/strict) to tune security strictness. The system detects homoglyph attacks, zero-width character injection, and ROT13/URL/base64 obfuscation bypasses. This level of security tooling is rare in open-source agent platforms.

Natural Language Recurring Tasks. Create recurring tasks with natural language — “every morning at 9am” or “every 2 hours” — and the built-in schedule parser converts them to cron expressions stored in task metadata. A template-clone pattern keeps the original as a template and spawns dated child tasks on schedule. This sounds simple but solves a real operational pain: most agent workflows need to run on schedules, and managing cron jobs outside the orchestration platform creates blind spots.

Skills Hub with Security Scanner. Browse, install, and manage agent skills from local directories and external registries (ClawdHub, skills.sh). The built-in security scanner checks for prompt injection, credential leaks, data exfiltration, obfuscated content, and dangerous shell commands before installation. Supports five skill roots across ~/.agents/skills, ~/.codex/skills, project-local directories, and ~/.openclaw/skills. Bidirectional disk-to-database sync keeps everything consistent.

Four-Layer Agent Evaluation. Output evals score task completion against golden datasets. Trace evals detect convergence loops and stuck agents. Component evals measure tool reliability with p50/p95/p99 latency percentiles. Drift detection flags agents whose performance drops more than 10% against a four-week rolling baseline. This evaluation framework is more thorough than what most teams build internally, and it runs continuously without manual intervention.

Use Cases

• Multi-agent development teams — Teams running Claude Code for code review, a custom LangGraph pipeline for testing, and CrewAI for documentation generation can manage all three from one dashboard with unified cost tracking and security auditing.
• AI-powered internal tools — Companies building internal tools powered by multiple AI agents (customer support triage, data analysis, content generation) can use Mission Control as the orchestration backbone with role-based access for operators and viewers.
• Research and experimentation — AI researchers running multi-agent experiments can track agent behavior, evaluate performance across runs, and manage the operational overhead of running dozens of agent instances simultaneously.
• Solo developers with multiple AI assistants — Even individual developers running Claude Code, Codex, and custom agents benefit from centralized session tracking, cost monitoring, and the Skills Hub for managing agent capabilities across projects.
• Production agent deployments — Teams deploying agents to production can use the hardened Docker profile, security audit panels, trust scoring, and quality gates to operate agents with the same rigor they’d apply to traditional services.

Pros and Cons

Pros:

• Genuinely self-hostable with zero external dependencies — no Redis, no Postgres, no Docker required for development. The SQLite-backed architecture keeps deployment simple while the WAL mode handles concurrent reads efficiently.
• Multi-framework support means you’re not locked into a single agent ecosystem. The adapter architecture for OpenClaw, CrewAI, LangGraph, AutoGen, and Claude SDK covers the most popular frameworks, and the pattern is extensible.
• Security tooling is production-grade, not an afterthought. The trust scoring, secret detection, MCP call auditing, and homoglyph/obfuscation detection show serious attention to the attack surface of AI agent systems.
• Active development with 279 commits from the lead maintainer, 577 tests (282 unit + 295 E2E), and three releases in four months. The project isn’t going dormant.

Cons:

• Alpha status with 173 open issues as of June 2026 means APIs and database schemas will change. The v1.x to v2.0 migration involved breaking changes, and more are likely as the project stabilizes.
• The 32-panel UI can feel overwhelming for teams that only need basic task management. There’s no way to hide unused panels, and the learning curve for the full feature set is steep.
• Single-maintainer dominance (279 of 314 total commits from 0xNyk) creates bus factor risk. The project needs more core contributors to sustain long-term development.
• No built-in visual workflow builder — orchestration rules are configured through the UI’s form-based system, not a drag-and-drop flow editor like n8n or Temporal’s workflow designer.

Getting Started

# One-command install (handles Node.js 22+, pnpm, dependencies)
git clone https://github.com/builderz-labs/mission-control.git
cd mission-control
bash install.sh --local

# Or manual setup
git clone https://github.com/builderz-labs/mission-control.git
cd mission-control
nvm use 22 && pnpm install
pnpm dev                    # http://localhost:3000/setup

# Or Docker
docker compose up           # auto-generates credentials

Open http://localhost:3000/setup to create your admin account. Register your first agent:

export MC_URL=http://localhost:3000
export MC_API_KEY=***   # shown in Settings after first login

# Register an agent
curl -X POST "$MC_URL/api/agents/register" \
  -H "Authorization: Bearer $MC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name": "scout", "role": "researcher"}'

# Create a task
curl -X POST "$MC_URL/api/tasks" \
  -H "Authorization: Bearer $MC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"title": "Research competitors", "assigned_to": "scout", "priority": "medium"}'

# Poll the queue as the agent
curl "$MC_URL/api/tasks/queue?agent=scout" \
  -H "Authorization: Bearer $MC_API_KEY"

For production, use the hardened Docker profile:

docker compose -f docker-compose.yml -f docker-compose.hardened.yml up -d

Alternatives

Langflow — A visual framework for building multi-agent workflows with a drag-and-drop interface. Langflow is better for designing agent pipelines visually and has a gentler learning curve, but it lacks Mission Control’s operational features: no cost tracking, no security auditing, no trust scoring, and no multi-framework gateway support. Choose Langflow when you’re designing workflows; choose Mission Control when you’re operating them in production.

CrewAI Enterprise — The commercial platform from the CrewAI team provides hosted orchestration with a polished UI and managed infrastructure. It’s more mature and better documented, but it’s SaaS-only with per-seat pricing. Mission Control is the self-hosted alternative for teams that need to keep their agent infrastructure on-premises or in their own cloud accounts. The trade-off is operational overhead — you’re running and maintaining the platform yourself.

n8n — The open-source workflow automation tool handles multi-step agent workflows with its visual flow editor and 400+ integrations. n8n is more versatile for general automation but doesn’t specialize in AI agent management — no trust scoring, no agent evaluation framework, no skills hub. Choose n8n when your workflows mix AI agents with traditional API integrations; choose Mission Control when AI agents are the primary workload and you need agent-specific operational tooling.

Verdict

Mission Control is the most complete open-source agent orchestration platform I’ve seen in mid-2026. The zero-dependency, SQLite-backed architecture is a refreshing departure from the Kubernetes-first approach that most orchestration tools take, and the multi-gateway support means you’re not betting everything on one agent framework. The security tooling — trust scoring, secret detection, homoglyph attack detection — shows the team understands that AI agents in production are a security surface, not just a productivity tool.

It’s alpha software with rough edges. The single-maintainer concentration is a real risk, and the 173 open issues suggest the API surface hasn’t stabilized. But the 5,300-star growth in four months, the 577-test suite, and the three releases in that timeframe signal a project that’s shipping consistently. If you’re running multiple AI agents and you’re tired of stitching together terminal tabs and spreadsheets to manage them, Mission Control is worth setting up this weekend. The install.sh --local path gets you running in under five minutes, and the SQLite backend means you can evaluate it without provisioning anything.