Stop Gluing AI Tools Together! Mission Control Unifies Your Agent Fleet

Your AI agents are running wild. One tool for task queues, another for cost tracking, a third for security audits, and half a dozen dashboards that never quite sync. You've stitched together Redis, Postgres, and three different monitoring solutions just to watch your agent fleet stumble through workflows. The infrastructure bill keeps climbing. The data stays fragmented. And when something breaks at 2 AM, you're hunting through five different logs praying for a clue.

Sound painfully familiar? You're not alone. The AI agent ecosystem exploded so fast that orchestration became an afterthought. Teams bolted on tools reactively, creating technical debt that compounds with every new agent deployment. But what if a single platform could dispatch tasks, run multi-agent workflows, monitor spend, and govern operations—all from one unified dashboard, with zero external dependencies, powered by nothing more than SQLite?

Enter Mission Control by Builderz Labs: the open-source, self-hosted AI agent orchestration platform that's making fragmented agent stacks obsolete. No Redis. No Postgres. No Docker required unless you want it. Just pure, focused orchestration that ships with 32 operational panels, real-time everything, and production-grade security out of the box. Built on Next.js 16 and TypeScript 5.7, battle-tested with 577 tests (282 unit + 295 E2E), and designed for teams who refuse to compromise between speed and control.

Ready to reclaim your sanity? Let's dive deep into why top engineering teams are abandoning their patchwork setups for Mission Control—and how you can deploy your own control center before your coffee gets cold.

What Is Mission Control?

Mission Control is an open-source dashboard for AI agent orchestration created by Builderz Labs, a team that's shipped 32+ products across 15 countries and knows exactly where agent infrastructure breaks down. At its core, it's a single-page application shell that unifies every operational concern of running AI agent fleets: task dispatch, multi-agent workflow coordination, cost tracking, security governance, memory management, and real-time monitoring.

What makes it genuinely disruptive? Radical simplicity through self-containment. While competitors demand Kubernetes clusters, managed databases, and half a dozen microservices, Mission Control runs on SQLite with WAL mode via better-sqlite3. One pnpm start command boots the entire platform. The installer auto-generates secure credentials, handles Node.js 22+ and pnpm setup, and drops you onto a setup page at localhost:3000 within minutes.

The project is currently in alpha under active development, with APIs and schemas evolving between releases. But don't let "alpha" fool you—this is production-hardened software with comprehensive security documentation, hardened Docker deployment profiles, and a four-layer agent evaluation framework that many GA products lack entirely.

Mission Control is trending now because it solves the orchestration gap that frameworks like LangGraph, CrewAI, and AutoGen create. These tools excel at building agent logic but leave teams stranded on operations: How do you track costs across 50 agents? How do you enforce quality gates before task completion? How do you audit MCP tool calls for security? Mission Control answers all of these without forcing you to re-platform your existing agents.

Key Features That Eliminate Operational Chaos

Mission Control packs 32 feature panels into a unified interface. Here's where it gets technically interesting:

Real-Time Telemetry Without the Infrastructure Tax The platform uses WebSocket + Server-Sent Events (SSE) for push updates, with intelligent polling that pauses when you're away. This isn't just battery-friendly—it's cost-effective. No persistent connections burning compute when nobody's watching. Zero stale data when they return. Compare this to WebSocket-only architectures that maintain expensive connections for idle dashboards.

Zero External Dependencies SQLite handles all persistence. No Redis for caching, no Postgres for relational data, no Elasticsearch for logs. The entire state lives in .data/ with WAL mode for concurrent read performance. This isn't a toy architecture—it's the same pattern that powers production systems at MUTX, which ported and extended Mission Control for their agent infrastructure platform.

Role-Based Access with Google Sign-In Three roles—viewer, operator, admin—with session cookie auth (7-day expiry) and API key access for headless operations. Google OAuth includes an admin approval workflow, preventing unauthorized domain users from gaining access. Authentication uses scrypt hashing, not bcrypt or worse, PBKDF2.

Aegis Quality Gates Built-in review system that blocks task completion without sign-off. Tasks flow through six Kanban columns: inbox → assigned → in progress → review → quality review → done. No more agents marking their own homework complete.

Skills Hub with Security Scanning Browse and install skills from ClawdHub and skills.sh registries. Before installation, a security scanner checks for prompt injection vectors, credential leaks, data exfiltration patterns, obfuscated content, and dangerous shell commands. Bidirectional sync between disk and database means your ~/.agents/skills directory stays in sync with the platform.

Multi-Gateway Architecture Connect to multiple agent gateways simultaneously through framework adapters for OpenClaw, CrewAI, LangGraph, AutoGen, and Claude SDK. Each adapter normalizes registration, heartbeats, and task reporting to a common interface. This is how you integrate existing agents without re-platforming.

Natural Language Scheduling Type "every morning at 9am" or "every 2 hours"—the built-in parser converts to cron expressions using a template-clone pattern that keeps originals as templates and spawns dated child tasks on schedule.

Four-Layer Agent Evaluation

• Output evals: Task completion scoring against golden datasets
• Trace evals: Convergence and infinite loop detection
• Component evals: Tool reliability with p50/p95/p99 latency percentiles
• Drift detection: 10% threshold against 4-week rolling baselines

Use Cases: Where Mission Control Dominates

1. The 5-Minute Local Control Center

You're a solo developer or small team who needs agent visibility now. Run bash install.sh --local, create your admin account at /setup, and register your first agent through the UI. No cloud services to configure, no credit cards to enter. Your entire agent operation lives on your machine, with full cost tracking from day one.

2. Multi-Agent Workflows with Quality Assurance

Build specialist agent teams: a researcher, a coder, a reviewer. Enable orchestration rules and the Aegis quality review gate. Track handoffs end-to-end in the Kanban board. When the reviewer agent flags concerns, completion blocks until human or elevated-agent approval. This is how you prevent autonomous agents from shipping broken code or hallucinated research.

3. Production Operations with Security Hardening

Deploy with docker-compose.hardened.yml for read-only filesystems, capability dropping, HSTS headers, and network isolation. Configure MC_ALLOWED_HOSTS and TLS reverse proxy. Monitor the trust score panel (0-100) and security audit feeds continuously. Secret detection runs across all agent messages. MCP tool calls get audited. Hook profiles (minimal/standard/strict) let you tune security strictness per deployment environment.

4. CLI Agent Integration Without Re-Platforming

Already using Claude Code or Codex? Connect via the read-only CLI integration that surfaces sessions, tasks, and configs from ~/.claude/projects/ and ~/.claude/tasks/ on the dashboard. Keep your existing workflows while adding centralized observability and controls. The platform auto-discovers local sessions and extracts token usage, model info, and cost estimates.

Step-by-Step Installation & Setup Guide

One-Command Install (Recommended)

# Clone and enter the repository
git clone https://github.com/builderz-labs/mission-control.git
cd mission-control

# Run the automated installer for local development
bash install.sh --local     # Or: bash install.sh --docker

The installer handles:

• Node.js 22+ verification and installation via nvm
• pnpm package manager setup
• Dependency installation
• Secure credential auto-generation

After installation completes:

open http://localhost:3000/setup    # Create your admin account

Manual Setup (For Customization)

git clone https://github.com/builderz-labs/mission-control.git
cd mission-control
nvm use 22 && pnpm install          # Ensure correct Node version
pnpm dev                            # Development server at localhost:3000/setup

Docker Zero-Config (Fastest to Share)

docker compose up                   # Auto-generates credentials, persists across restarts

Production-Hardened Deployment

# Layer the hardened profile for security-critical deployments
docker compose -f docker-compose.yml -f docker-compose.hardened.yml up -d

This enables: read-only root filesystem, dropped Linux capabilities, HSTS headers, and network isolation. Critical: Configure MC_ALLOWED_HOSTS and deploy behind a TLS reverse proxy before any network exposure.

Prebuilt Images (CI/CD Friendly)

# Pull from GitHub Container Registry
docker pull ghcr.io/builderz-labs/mission-control:latest

# Run with ephemeral container
docker run --rm -p 3000:3000 ghcr.io/builderz-labs/mission-control:latest

Windows PowerShell Install

.\install.ps1 -Mode local

Post-Installation Verification

# Run the built-in health diagnostic
bash scripts/station-doctor.sh

# Run security configuration audit
bash scripts/security-audit.sh

REAL Code Examples from the Repository

Example 1: Registering Your First Agent via API

The README provides a complete curl-based workflow for agent registration and task creation. Here's the exact pattern with detailed annotation:

# Set environment variables for API access
export MC_URL=http://localhost:3000
export MC_API_KEY=your-api-key   # Displayed in Settings after first login

# Register a new agent with researcher role
# The 'scout' agent can now poll for tasks and report heartbeats
curl -X POST "$MC_URL/api/agents/register" \
  -H "Authorization: Bearer <MC_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "scout",
    "role": "researcher"
  }'

# Response includes agent_id, registration_token, and heartbeat_interval

This endpoint creates the agent record in SQLite, initializes trust scoring at 50/100, and registers the agent for task queue polling. The researcher role maps to default permissions in the RBAC system—view tasks, create memory entries, but no administrative operations.

Example 2: Creating and Assigning Tasks

# Create a task assigned to our scout agent
# Priority levels: low, medium, high, critical
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"
  }'

# The task enters the 'inbox' column of the Kanban board
# Aegis quality gate is enabled by default for medium+ priority

The task creation triggers WebSocket push to all connected dashboards. If the agent is online, it receives an immediate notification. The assigned_to field creates the agent-task binding that appears in the queue endpoint.

Example 3: Agent Task Queue Polling

# Poll for tasks assigned to scout agent
# Returns pending tasks sorted by priority and creation time
curl "$MC_URL/api/tasks/queue?agent=scout" \
  -H "Authorization: Bearer <MC_API_KEY>"

# Agent claims task by updating status to 'in_progress'
# Then submits results for quality review

This polling mechanism is how headless agents integrate. The queue endpoint respects RBAC—agents can only see their own assignments. For real-time push instead of polling, agents can connect to the WebSocket gateway.

Example 4: Gateway-Optional Mode for Restricted Environments

# Run without any gateway connection
# Perfect for VPS deployments with firewall restrictions
# or when using Mission Control purely for project/task operations
NEXT_PUBLIC_GATEWAY_OPTIONAL=true pnpm start

In this mode, task boards, projects, agents, sessions, scheduler, webhooks, alerts, and cost tracking all function normally. Only real-time session updates and agent messaging require an active gateway. This is the deployment pattern for air-gapped or compliance-restricted environments.

Example 5: Docker Hardened Compose Configuration

# Production security profile layers on top of base compose
docker compose -f docker-compose.yml -f docker-compose.hardened.yml up -d

The hardened profile implements:

• Read-only root filesystem: Prevents runtime mutation of container image
• Dropped capabilities: Removes unnecessary Linux kernel privileges
• HSTS headers: Enforces HTTPS for browser clients
• Network isolation: Restricts inter-container communication to declared services

Example 6: Development Quality Gate

# Run complete quality verification before commits
pnpm quality:gate     # Executes typecheck, lint, unit tests, and E2E tests

This single command runs: TypeScript compilation check, ESLint, 282 Vitest unit tests, and 295 Playwright E2E tests. The CI/CD pipeline uses this same gate, ensuring local and remote quality standards match.

Advanced Usage & Best Practices

Trust Score Tuning The security panel shows per-agent trust scores (0-100). New agents start at 50. Monitor the drift detection panel for behavior anomalies—a 10% threshold against 4-week baselines catches gradual degradation before it becomes critical. For high-risk operations, enforce strict hook profiles that audit every MCP call and block suspicious patterns.

Memory Knowledge Graph Optimization The filesystem-backed memory tree in ~/.agents/memory syncs bidirectionally with SQLite. For large knowledge bases, prune relationship graphs monthly using the Memory Browser panel. The interactive visualization helps identify orphaned memory chunks that bloat storage without adding value.

Cost Tracking by Model The Recharts-powered dashboard breaks token usage by model. Use this to identify expensive model calls that could be downgraded—Claude 3 Opus for research, Haiku for summarization. Session-level granularity catches runaway costs from infinite loops or retry storms.

Workspace Isolation for Multi-Tenant Scenarios The /api/super/* endpoints create isolated tenant environments with dedicated gateways and state directories. Each workspace gets independent SQLite databases—no data leakage between clients. Monitor provisioning jobs through the same dashboard that manages your primary workspace.

Webhook Reliability Patterns Outbound webhooks implement exponential backoff with circuit breaker protection. For critical integrations, verify HMAC-SHA256 signatures on receipt. The delivery history panel shows retry counts and final disposition—essential for debugging failed Slack notifications or GitHub issue syncs.

Comparison with Alternatives

The verdict: LangSmith and Langfuse excel at LLM tracing but leave orchestration to you. Mission Control replaces the entire operational layer that tracing tools assume you already built. For teams without dedicated platform engineers, this is the difference between shipping agent features this sprint versus next quarter.

FAQ

Q: Is Mission Control production-ready despite the alpha label? A: The alpha designation indicates API/schema evolution, not instability. The platform includes hardened Docker profiles, comprehensive security documentation, and 577 automated tests. Review SECURITY.md and follow deployment hardening guidelines before production use.

Q: Can I use Mission Control without any existing agent framework? A: Absolutely. The gateway-optional mode runs standalone. Register agents via REST API, create tasks through the UI, and track everything without connecting LangGraph, CrewAI, or any other framework.

Q: How does SQLite handle concurrent agent operations? A: WAL (Write-Ahead Logging) mode enables concurrent reads during writes. For read-heavy workloads typical of dashboard usage, performance matches or exceeds client-server databases. The better-sqlite3 driver provides synchronous, high-performance access.

Q: What's the migration path if I outgrow SQLite? A: The database abstraction in src/lib/db.ts isolizes SQLite specifics. While no automatic Postgres migration exists yet, the schema is well-documented across 39 migration files. Community contributions for additional database backends are welcomed per CONTRIBUTING.md.

Q: How do I secure the admin credentials generated by the installer? A: The installer auto-generates secure credentials, but change all defaults before network exposure. Use AUTH_PASS_B64 for passwords containing special characters like #. Never expose to public internet without MC_ALLOWED_HOSTS and TLS reverse proxy.

Q: Can multiple team members collaborate on the same Mission Control instance? A: Yes, through workspace isolation (/api/super/* endpoints) or shared single-tenant deployments with RBAC. The Google Sign-In with admin approval workflow enables controlled team expansion.

Q: What happens when agent tasks fail or hang? A: The trace evaluation layer detects convergence failures and infinite loops. Tasks in in_progress beyond configured timeouts appear in the security audit panel with elevated trust score impact. Operators can force-cancel through the UI or API.

Conclusion: Take Command of Your Agent Fleet

The fragmented AI agent toolchain is a tax on engineering velocity that compounds with every new deployment. Mission Control demolishes that complexity with radical self-containment: one platform, one command to run, 32 operational panels, and the security rigor that production systems demand.

What impresses most isn't any single feature—it's the integration depth. The Aegis quality gates, four-layer evaluation framework, skills security scanner, and Claude Code bridge aren't bolted-on afterthoughts. They're co-designed into an architecture that treats operations as a first-class concern, not an infrastructure chore to outsource.

For teams building with LangGraph, CrewAI, AutoGen, or custom SDKs, Mission Control is the missing operational layer. For teams starting fresh, it's the fastest path from idea to governed, observable agent fleet. The zero external dependency philosophy means you can evaluate it on a laptop, prove value in a day, and scale to hardened production without architectural rewrites.

The alpha stage means you shape its evolution. Open issues, submit PRs, or simply star the repository to signal interest. The Builderz Labs team ships fast—32 products worth of shipping experience informs every commit.

Stop gluing tools together. Start orchestrating with intention.

👉 Get Mission Control on GitHub — clone it, run bash install.sh --local, and have your first agent registered before your next meeting.

Built something amazing with Mission Control? The team wants to feature you. Open an issue with the showcase label or tweet @nyk_builderz. For commercial agent infrastructure needs, Builderz Labs builds production systems across AI, trading, and Solana ecosystems.