Mission Control is live · free plan, paid from $5,000–$12,000 one-time · last verified 24 Oct 2026 · viability monitor
Mission Control
Self-hosted AI agent control plane for live session replay, task orchestration and per-agent cost tracking
One of the few agent-ops tools that states its own limits up front: alpha, self-hosted, single maintainer. For a team running several agents across CrewAI, LangGraph or OpenClaw, getting live replay, task state, per-agent spend and RBAC for a $0 licence fee is a real bargain. Just don't route revenue-critical work through it without a rollback plan and someone who can read a changelog.
Verdict generatedVerified 5d ago · liveness 64/100 · cite: rightaichoice.com/tools/mission-control
- Platform teams coordinating several agents built on OpenClaw, CrewAI, LangGraph, AutoGen or the Claude SDK
- Operators who want sessions, task state and per-agent token spend in one self-hosted dashboard instead of four tools
- Organizations with data-residency or air-gap rules that rule out hosted agent observability services
- Developers comfortable with git clone installs, SQLite local mode and Docker deployments
- Solo builders running one agent on one machine — the vendor itself says a control plane is probably unnecessary
- Teams that need a managed service with a vendor accountable for uptime
- Production teams that can't absorb schema and API changes while the project is alpha
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Skip Mission Control if you run a single agent on one machine, need a managed service with a vendor on the hook for uptime, or require stable production APIs today — the vendor lists all three as reasons to skip it.
Self-hosting means you own the bill the vendor doesn't send: compute, storage, backups and the engineer-hours to keep the instance patched and upgraded.
The MIT-licensed distribution runs at $0/mo in license cost — you pay only for the infrastructure you run it on. That puts it well below hosted agent-observability SaaS on sticker price, and the trade is that you supply the ops. Teams that want the wider agent operating stack installed for them are looking at the vendor's fixed-scope Agent OS Setup services package, quoted at $5,000-$12,000 one-time with delivery typically in 2-4 weeks.
In short
Mission Control — Self-hosted AI agent control plane for live session replay, task orchestration and per-agent cost tracking. Best for Platform teams coordinating several agents built on OpenClaw, CrewAI, LangGraph, AutoGen or the Claude SDK, Operators who want sessions, task state and per-agent token spend in one self-hosted dashboard instead of four tools, Organizations with data-residency or air-gap rules that rule out hosted agent observability services. Free to start; paid plans from $5,000–$12,000 one-time.
What is Mission Control?
Mission Control — Self-hosted AI agent control plane for live session replay, task orchestration and per-agent cost tracking. Best for Platform teams coordinating several agents built on OpenClaw, CrewAI, LangGraph, AutoGen or the Claude SDK, Operators who want sessions, task state and per-agent token spend in one self-hosted dashboard instead of four tools, Organizations with data-residency or air-gap rules that rule out hosted agent observability services. Free to start; paid plans from $5,000–$12,000 one-time.
Mission Control is an MIT-licensed, self-hosted dashboard for teams whose fleet of AI agents has outgrown scripts, log tailers and a spend spreadsheet. It gathers sessions, tasks, pipelines, logs, memory, cost, approvals and governance into one operator surface, and it's explicit that it's alpha software maintained by an independent open-source builder (nyk) — roughly 6.4k GitHub stars, 120 forks, no account and no forced product telemetry. Install is a git clone plus `bash install.sh --local`; the installer handles Node.js, pnpm, dependencies and credentials. Two runtime modes are documented: SQLite local mode for a laptop and a hardened Docker profile for a shared box. The vendor's own advice is to start local and add gateways and advanced controls only when your operating model demands them. The product splits into four layers. Run covers a six-column task board with priorities, threaded comments, projects and sub-agent spawning, dispatch to CLI agents with the linked execution session retained, multi-step pipelines, and natural-language recurring schedules that become dated tasks and cron runs. Observe gives live session replay and debugging, a memory knowledge graph, per-agent token and cost breakdowns, and Claude Code session auto-discovery. Control wraps RBAC, audit history, trust scoring, secret detection, alert rules and Aegis quality gates with automated review. Connect ships adapters for OpenClaw, CrewAI, LangGraph, AutoGen, Claude SDK and a generic fallback, plus WebSocket, REST and SSE transports, HMAC-SHA256 signed webhooks with a circuit breaker, multi-gateway OS-level discovery and bidirectional GitHub Issues sync — exposed across 32 operator panels and 101 OpenAPI routes. Positioning: cheaper and more hands-on than hosted agent observability services, more purpose-built for agent operations than wiring Grafana to a kanban tool, and less turnkey than managed platforms.
Key features
- Live session replay and debugging across agent runs
- Memory knowledge graph visualization
- Per-agent token usage and cost breakdowns
- Claude Code session auto-discovery
- Six-column kanban with priorities, threaded comments and projects
- Sub-agent spawning directly from tasks
- Task dispatch to CLI agents with the linked execution session retained
- Multi-step pipeline start and monitoring
- Natural-language recurring schedules converted to dated tasks and cron runs
- Aegis quality gates with automated review
- RBAC, audit history, trust scoring and secret detection
- Alert rules for agent operations
- Bidirectional GitHub Issues sync
- Webhooks with HMAC-SHA256 signing and circuit breaker
- Multi-gateway with OS-level discovery
Behind the verdict
Mission Control earns a slot when you've crossed the line from one agent on a laptop to several agents, several people, and handoffs that get lost in Slack. The tell is when you can't answer "what is each agent doing right now, and what did it cost today?" without opening four tabs. That's the problem this closes. We'd pick it for the pairing of live session replay with per-agent token and cost breakdowns. Those two together answer the most common incident question — which run burned the budget and why — in one place. Dispatch that keeps the linked execution session, plus Aegis quality gates and secret detection, is the second reason: the guardrails ship with the orchestration instead of arriving as a later integration project. Pass if you need a vendor on the hook for uptime, or if your production stack can't tolerate changing schemas. The page says as much: alpha, APIs may move, review the deployment and security guides before exposing it. A group standardized on Jira should also look elsewhere — GitHub Issues is the only documented tracker sync. The newest competitive pressure comes from elsewhere too: OneCLI, a YC-backed open-source sandboxed agent harness that launched in August 2026, targets teams weighing sandboxing against control-plane visibility. Compared with hosted observability services, you trade a support contract for infrastructure you own and a licence bill of zero. Compared with a Grafana-plus-kanban build, you trade flexibility for something already shaped like agent operations. Where it bites is the operational chore of upgrades: budget real time to read release notes, test in Docker, and keep a known-good snapshot.
Models under the hood
as of 2026-10-24
Limitations
- Mission Control is explicitly self-hosted alpha software (MIT licensed), and the vendor recommends skipping it if you run one agent on one machine, need a managed SaaS, or require stable production APIs today.
- Setup is a hands-on engineering step (git clone, install.sh, Node.js, pnpm, optional Docker), and local data with SQLite means operational state lives on your own infrastructure under your own upgrade risk.
as of 2026-10-05
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Is Mission Control still active?
Mission Control is live · free plan, paid from $5,000–$12,000 one-time · last verified 24 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Mission Control? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Mission Control?
What people actually say about Mission Control — is it worth it?
Our community sentiment research written Jul 29, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Mission Control— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Mission Control 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page, limitations, our verdict, features, summary, pricing tiers, description
- — changed our verdict, description, integrations, summary, who it suits, who should skip it, features, pricing tiers
- — changed limitations, pricing page
- — changed pricing tiers, integrations, who it suits, summary, our verdict, who should skip it, features, description
- — changed pricing page, limitations
- — changed pricing tiers, who should skip it, setup time, pricing fit, description, who it suits, summary, our verdict, features, hidden costs, use cases, limitations, switching in, switching out
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Mission Control cost in 2026?
Mission Control: Free to start; paid plans from $5,000–$12,000 one-time. Pricing last verified 24 Oct 2026.
Plans compared
Every published Mission Control tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Self-Hosted (Open Source) | Free | — | Platform teams running multiple agents across OpenClaw, CrewAI, LangGraph, AutoGen or Claude SDK who can run Node.js and Docker on their own infrastructure.Adds: Starting tier at $0/mo — MIT-licensed source, no account required, local SQLite mode for single-machine setups, hardened Docker profile for production, and all 32 operator panels and 101 OpenAPI routes. |
| Agent OS Setup | $5,000–$12,000 one-time | one-time | — |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Mission Control's pricing actually pencils out — and where peers do it cheaper.
The MIT-licensed distribution runs at $0/mo in license cost — you pay only for the infrastructure you run it on. That puts it well below hosted agent-observability SaaS on sticker price, and the trade is that you supply the ops. Teams that want the wider agent operating stack installed for them are looking at the vendor's fixed-scope Agent OS Setup services package, quoted at $5,000-$12,000 one-time with delivery typically in 2-4 weeks.
Has Mission Control's pricing changed?
No plan price for Mission Control has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Mission Control?
Pick Mission Control if you are Platform teams coordinating several agents built on OpenClaw, CrewAI, LangGraph, AutoGen or the Claude SDK, Operators who want sessions, task state and per-agent token spend in one self-hosted dashboard instead of four tools, Organizations with data-residency or air-gap rules that rule out hosted agent observability services, Developers comfortable with git clone installs, SQLite local mode and Docker deployments.
Look elsewhere if you are Solo builders running one agent on one machine — the vendor itself says a control plane is probably unnecessary, Teams that need a managed service with a vendor accountable for uptime, Production teams that can't absorb schema and API changes while the project is alpha, Buyers who require stable production APIs today, per the vendor's own skip-it guidance.
Skip it if Skip Mission Control if you run a single agent on one machine, need a managed service with a vendor on the hook for uptime, or require stable production APIs today — the vendor lists all three as reasons to skip it.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Mission Control actually fits — and what changes day-one when you adopt it.
You clone the repo and run `bash install.sh --local`, which provisions Node.js, pnpm, dependencies and credentials, then let Claude Code session auto-discovery populate the session view before you wire any adapters.
Outcome: You get a live view of sessions, logs, memory and per-agent token cost on day one, without standing up a bespoke dashboard first.
You move queued agent work through the six-stage task board, assign owners, comment in threads, dispatch jobs to CLI agents so each task links to its execution session, and set recurring jobs in natural language that resolve to cron runs.
Outcome: Task ownership and execution state live in one place, and scheduled agent work stops living in scattered crontabs and shell scripts.
You enable RBAC scoping, audit history, trust scoring, secret detection and alert rules, and route output through Aegis quality gates with automated review before it ships.
Outcome: Autonomous agent work has an approval trail and a gate, which is what makes it defensible in a review or incident postmortem.
Use cases
- Watch every active agent session, log and memory entry from one dashboard instead of tailing separate terminals.
- Move agent work through a six-stage kanban with owners, priorities and threaded review comments.
- Schedule recurring agent work in plain English — "every Monday at 9am" — and have it land as dated tasks and cron runs.
- Gate agent output with Aegis automated review before it reaches production.
- Auto-discover Claude Code sessions to get cost and usage visibility without integrating anything first.
- Sync GitHub Issues bidirectionally so tracker state and agent operations stay aligned.
- Give operators RBAC-scoped access and keep an audit trail of every agent action and approval.
Setup time & first value
How long it actually takes to get something useful out of Mission Control — broken out by persona, not the marketing-page minute.
For a developer comfortable with Node.js and Docker: clone the repo and run `bash install.sh --local` to reach a working local instance with SQLite in well under an hour. Production-hardened Docker deployment plus adapters, RBAC and alert rules is a day-plus of configuration, since every adapter and gateway is wired by hand. If you take the Agent OS Setup services package, the vendor quotes
Switching to or from Mission Control
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From crontab and shell scripts: replace scheduled `run-agent.sh` jobs with natural-language recurring schedules that resolve to dated tasks and cron runs.
- →From scattered log tailing: point adapters at OpenClaw, CrewAI, LangGraph, AutoGen or Claude SDK and watch sessions, logs and memory in one control surface.
- →From a manual spend spreadsheet: use per-agent token and cost breakdowns instead of reconciling provider invoices by hand.
- →From GitHub Issues tracked separately: turn on bidirectional sync so tracker state and agent operations stay aligned.
- →From a homegrown internal dashboard: replace it with the 32 operator panels and 101-route OpenAPI contract the project already ships.
- ↗To a managed agent-observability SaaS: export session and task records and rebuild dashboards in the hosted product if you need a vendor accountable for uptime.
- ↗To plain LLM tracing: keep the tracing layer and drop the orchestration board if all you need is span-level logs.
- ↗To Jira plus CI: move the task board into Jira and keep only the Aegis quality gates in the pipeline if your org standardizes on Atlassian.
Integrations
Resources & guides
Tutorials & Learning
Official links
What are the best Mission Control alternatives?
The closest alternatives to Mission Control we list are Traccia, Aembit and Fact0. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Traccia | Freemium | Pricing not published | — |
| Aembit | Freemium | Pricing not published | — |
| Fact0 | Freemium | Pricing not published | — |
Alternatives to Mission Control
View all
Tools that pair well with Mission Control
Common stack mates teams adopt alongside Mission Control, with the specific reason each pairing earns its keep.
Traccia
Traccia is an AI agent control plane that traces, costs, evaluates and enforces policy on agents you already run.
Aembit
Identity control plane that brokers short-lived, policy-scoped credentials for AI agents, MCP servers, and non-human workloads.
Fact0
Open-source agent audit and replay: hash-chained records, signed exports, self-hosted on your own Docker Compose stack.
Mission Control vs its closest rival
Mission Control's closest head-to-head is with Spider Cloud — These tools aren't competitors—they solve different problems. Spider Cloud is a web data extraction API for feeding AI agents, while Mission Control is an orchestration dashboard for managing those agents. If you need to pull structured data from the web for LLMs, choose Spider Cloud. If you need to coordinate, monitor, and govern multiple AI agents, go with Mission Control. Verdict generated
Head-to-head comparisons
Mission Control vs Spider Cloud
These tools aren't competitors—they solve different problems. Spider Cloud is a web data extraction API for feeding AI agents, while Mission Control is an orchestration dashboard for managing those agents. If you need to pull structured data from the web for LLMs, choose Spider Cloud. If you need to coordinate, monitor, and govern multiple AI agents, go with Mission Control.
GeneratedMission Control vs Temporal Ai
If your team needs bulletproof reliability for long-running AI agents and microservices, choose Temporal AI — its durable execution and state persistence are unmatched. If you need a lightweight, open-source dashboard for managing and observing multiple agents without infrastructure overhead, Mission Control is ideal. Temporal is enterprise-ready; Mission Control is for technical teams that want full control.
GeneratedMission Control vs Presto Voice
Choose Presto Voice if you run a QSR chain needing a proven drive-thru voice AI to boost revenue and efficiency; it's specialized and enterprise-focused. Choose Mission Control if your technical team needs an open-source dashboard to orchestrate and monitor AI agents with full control and customization.
GeneratedFrequently asked questions
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