Capy
Capy orchestrates fleets of cloud coding agents inside Slack, GitHub, and Linear, with unlimited members on every plan.
Pick Capy if your bottleneck is review bandwidth and a backlog of parallel chores — triage, migrations, E2E runs — not if you want one smart autocomplete in a local editor. The inbuilt Review agent that runs on every PR is the standout feature, and the no-seat-fee model is genuinely friendly to growing teams. Compare it against Cursor or Claude Code if you want a single-threaded local assistant, and against Codex CLI if you want a plain terminal harness — Capy's own Terminal-Bench graphic puts its harness at 82.1% versus 83.1% for Codex CLI. Budget for the credit layer and the ramp-up.
Verified 11d ago · liveness 70/100 · cite: rightaichoice.com/tools/capy
- Teams drowning in PR review who want an agent on every pull request
- Engineering orgs running wide backlogs: triage, migrations, refactors, dependency sweeps
- Companies that want unlimited-member plans sized by credits rather than seats
- Teams already paying for ChatGPT Codex, Copilot, or SuperGrok who want to reuse those plans
- Solo beginners who want inline autocomplete in a local editor
- Teams that need an offline or fully local IDE workflow
- Organizations requiring on-premise deployment
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Skip Capy if you need a fully local or on-premise coding setup, or if a credit-metered bill shared across the whole org is a model your finance team won't accept.
Credits cover both AI model costs and VM compute time, so long-running agent fleets burn balance faster than a single chat session would.
Capy fits teams that want many parallel agents without per-seat billing: Lite at $20/mo ($16/mo annual) is cheap enough to trial, Pro at $100/mo ($80/mo annual) is the daily-shipping tier, and Max steps $200–$1,000/mo for teams running wide parallel workloads. Cursor and Claude Code charge per developer seat, which gets expensive as headcount grows; Capy's unlimited-members model inverts that. Enterprise is custom-priced for BYOK, SSO/SAML, and audit logging.
In short
Capy — Capy orchestrates fleets of cloud coding agents inside Slack, GitHub, and Linear, with unlimited members on every plan. Best for Teams drowning in PR review who want an agent on every pull request, Engineering orgs running wide backlogs: triage, migrations, refactors, dependency sweeps, Companies that want unlimited-member plans sized by credits rather than seats. Plans from $20/mo.
What's new in Capy
Checked yesterdayAcross the latest 4 updates: 1 feature update, 1 pricing change and 2 changelog entries.
Capy 0.4.4: Plugins marketplace and guided PR review
Capy adds a plugin marketplace with MCP servers and skills available in every thread, plus guided review that opens each PR on an overview with before/after views.
Capy 0.4.3: Subagents rename and full GPT-6 context on Codex
Child agents are now called subagents across app, CLI, Slack and docs. On a Codex subscription, GPT-6 models use their full 1.05M-token context, up from a 400K cap.
Connect Capy to your Tailscale tailnet
Capy documents connecting to a Tailscale tailnet, letting threads reach services inside a private network.
New Capy Pro tiers: subscriptions that scale with you
Capy restructures its Pro plans into tiers that scale by usage.
What people actually say about Capy — is it worth it?
We scanned public community sources for Capy on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Capy? 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 →Key Features
- Parallel fleet orchestration: run many coding agents at once
- Sandboxed VMs with an isolated git branch per agent
- Captain agent for planning and triage
- Build agent for writing code
- Review agent that runs on every pull request
- Native GitHub integration: auto-branch, PRs, review comments
- Slack integration: create tasks and sync context from messages
- Linear integration: sync issues and update status
- Vercel integration: surface preview deployments
- Automation triggers on Slack messages, GitHub comments, or cron
- Browser use and computer use for end-to-end testing
- Annotated videos of each agent run
- Bring your own subscription: ChatGPT (Codex), GitHub Copilot, SuperGrok
- Model-agnostic across Claude, GPT, and Gemini
- Desktop apps for macOS, Windows, and Linux
About Capy
Capy is a cloud platform for engineering teams that want to move a lot of code at once. Instead of chatting with one assistant in a tab, you dispatch fleets of coding agents, each running in its own sandboxed VM on an isolated git branch, so dozens of tasks proceed side by side without stepping on each other. Three agent roles split the labor: Captain handles planning and triage, Build writes the code, and Review runs automatically on every pull request. Work is organized around tasks and threads tied to the apps your team already uses — a Slack message, a GitHub comment, or a Linear issue can start an agent, and a cron schedule can keep recurring security audits and cleanup running in the background. Model choice stays open across Claude, GPT, and Gemini. Bring-your-own-subscription is the money-saver: connect ChatGPT (Codex), GitHub Copilot, or SuperGrok and compatible models draw on plans you already pay for instead of your Capy credits. Every plan carries unlimited members, so Capy sizes credits rather than seats — Lite runs $20/mo ($16/mo annual), Pro $100/mo ($80/mo annual), and Max steps from $200 to $1,000/mo in $100 increments, with 10% bonus credits at every step. It is cloud-only, credits are shared org-wide and don't roll over, and orchestration has a real learning curve.
Behind the Verdict
Capy's core bet is throughput, not cleverness. The homepage pair is telling: a Review agent flagging real issues like plaintext credential records and a session cookie missing Secure and SameSite flags, and an E2E testing loop using native computer use, browser use, and annotated videos of each run. Those are the two places engineering teams lose the most wall-clock time, and Capy puts an agent in each. The architecture is the differentiator. Each agent gets its own sandboxed VM and its own git branch, so parallel frontend and backend work doesn't collide at merge time — the seed data describes parallelizing feature work across multiple agents "without merge conflicts using independent environments." Automation triggers on a Slack message, a GitHub comment, or cron, which turns recurring security audits and dependency sweeps into background jobs rather than calendar reminders. Pricing is structured around credits, not seats. Lite includes $20/mo of credits for $20/mo ($16/mo annual), Pro includes $105/mo for $100/mo ($80/mo annual), and Max includes 110% of its price at every step from $200 to $1,000/mo. Credits cover both AI model costs and VM compute time, are shared org-wide, and don't roll over — you can add balance at 1:1 value with a $5 minimum or turn on auto-reload. The honest tradeoffs: it's cloud-only, so anyone who needs a fully local or on-prem IDE workflow is out of scope. The orchestration model — tasks, threads, agent roles, triggers — takes real ramp-up compared to typing in an editor. And the credit ledger is a second mental model on top of your model subscriptions, even if bring-your-own-subscription softens it. If your work is one developer editing one file at a time, a single-threaded assistant is simpler and cheaper.
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Real-world workflow fit
Concrete scenarios for the personas Capy actually fits — and what changes day-one when you adopt it.
Your backlog has 200+ stale dependency and lint issues. You wire Capy to Linear, assign batches of issues to Build agents, and let the inbuilt Review agent comment on every resulting PR.
Outcome: Dozens of small PRs land in parallel on isolated branches, and reviewers only look at the diffs the Review agent has already flagged.
You turn on the Review agent for every pull request so diffs get analyzed and commented before a human opens them.
Outcome: Human reviewers spend their time on judgment calls instead of first-pass nitpicks, and the queue drains faster.
You set a cron automation to run nightly dependency sweeps and security audits, and route results into Slack.
Outcome: Recurring maintenance becomes background work, with findings posted where the team already talks.
Use Cases
- Automate bug fixes by assigning each issue to an agent that creates a branch, fixes the bug, and opens a PR for review.
- Generate a full careers page by describing requirements in a task; agents plan, build, and push the code.
- Parallelize frontend and backend feature work across multiple agents without merge conflicts using independent environments.
- Review and merge PRs faster by using Review agents to analyze diffs and leave comments.
- Turn a teammate's Slack message into a tracked task with its own agent and branch.
- Migrate infrastructure (e.g., server-action uploads to direct Supabase uploads) by delegating to an agent while you review.
- Run scheduled security audits and code cleanup as background agents on a cron trigger.
- Execute end-to-end tests with computer and browser use, captured as annotated videos.
Models Under the Hood
as of 2026-10-08
Limitations
- Capy is cloud-orchestrated around fleets of parallel coding agents, so its value depends on having enough tasks to justify delegating work rather than a single-threaded coding assistant.
- Pricing is credit-metered from $20/mo to $1,000/mo tiers, with credits covering both model usage and cloud machine capacity, and higher plans simply raise credit bonuses and machine limits.
- The platform is centered on the desktop app and web with integrations into Slack, GitHub, and Linear, so the workflow assumes those tools are already part of your team's process.
as of 2026-09-27
Verification history
We have re-verified Capy 7 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.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Plans compared
For each published Capy tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Capy Lite
$20/mo ($16/mo annual)
Ideal for
A small team or solo engineer who wants to trial parallel agents on real work before committing to a bigger credit budget.
What this tier adds
Starting tier: $20/mo of included credits, unlimited members, all three agents (Captain, Build, Review), and every supported model. First 7 days cost $1 with a verified card.
Capy Pro
$100/mo ($80/mo annual)
Ideal for
A team shipping every day that wants more headroom and the option to run compatible models on subscriptions it already pays for.
What this tier adds
Adds $105/mo of credits — a 5% bonus over the $100/mo price — plus custom environment and VM snapshots, and the ability to connect ChatGPT (Codex), GitHub Copilot, or SuperGrok subscriptions.
Capy Max
$200–$1,000/mo ($160–$800/mo annual)
Ideal for
Teams running genuinely wide parallel workloads who need credit headroom and the best bonus rate on a self-serve plan.
What this tier adds
Credits equal 110% of the plan price, a 10% bonus at every step, priced in $100 increments from $200 to $1,000/mo.
Enterprise
Custom
Ideal for
Larger organizations with compliance requirements, custom contracts, or a need to control model spend through their own provider keys.
What this tier adds
Adds a custom credit quota and invoicing, BYOK, SSO/SAML authentication, audit logging and compliance, and dedicated support.
Where the pricing makes sense
The company stage and team size where Capy's pricing actually pencils out — and where peers do it cheaper.
Capy fits teams that want many parallel agents without per-seat billing: Lite at $20/mo ($16/mo annual) is cheap enough to trial, Pro at $100/mo ($80/mo annual) is the daily-shipping tier, and Max steps $200–$1,000/mo for teams running wide parallel workloads. Cursor and Claude Code charge per developer seat, which gets expensive as headcount grows; Capy's unlimited-members model inverts that. Enterprise is custom-priced for BYOK, SSO/SAML, and audit logging.
Setup time & first value
How long it actually takes to get something useful out of Capy — broken out by persona, not the marketing-page minute.
Connecting Slack, GitHub, or Linear and dispatching a first task is quick — the first 7 days of Lite cost $1 with a verified card, so you can try it on real work. Realistic time to first useful PR is an afternoon. Teams chasing throughput across a wide backlog should budget longer: agent roles, task/thread structure, and trigger design are the parts that take weeks to get right.
Switching to or from Capy
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Cursor or Claude Code: keep your editor for hands-on edits and move wide backlog chores — triage, migrations, dependency sweeps — to Capy agents.
- →From Codex CLI: bring your ChatGPT (Codex) subscription to Capy so compatible models draw on a plan you already pay for.
- →From a manual PR review process: enable the inbuilt Review agent so every pull request gets a first-pass review automatically.
- →From GitHub Copilot: connect the Copilot subscription and run compatible models through Capy's parallel agent fleets.
- →From Linear-and-spreadsheets triage: connect Linear so issues sync into Capy tasks with their own agents and branches.
- ↗To Cursor or Claude Code: if you only ever need one developer editing one file at a time, move to a single-threaded local assistant.
- ↗To Codex CLI: if you want a plain terminal harness without VM orchestration or a credit ledger, a CLI is simpler to operate.
- ↗To self-hosted or on-prem tooling: necessary if compliance forbids cloud execution of your code.
- ↗To your existing editor's built-in agent: if adopting a task/thread/trigger model isn't worth it for your team size.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Capy”, and we withheld 6: 6 could not be judged, because “Capy” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Capy.
Official links
Tools that pair well with Capy
Common stack mates teams adopt alongside Capy, with the specific reason each pairing earns its keep.
Cursor
Cursor is your coding agent for building ambitious software — plan, build, test and ship with AI agents across IDE, CLI, Slack and cloud.
GitHub Copilot
GitHub Copilot is an AI coding agent that completes code, reviews pull requests, and runs Copilot, Claude, and Codex agents inside GitHub
Windsurf
Devin Desktop (formerly Windsurf) is an agent-native IDE for running fleets of local and cloud coding agents from one Sessions board.
Featured Head-to-Head Comparisons
Capy vs Locus Robotics
If you need to automate physical warehouse fulfillment—picking, putaway, replenishment—Locus Robotics is the proven choice with 2-3x productivity gains and deep WMS integrations. If you need to accelerate software development by orchestrating dozens of AI coding agents in parallel, Capy's multi-model approach and GitHub/Slack/Liner integration deliver unmatched throughput for engineering teams. They solve fundamentally different problems; pick based on whether your bottleneck is moving boxes or shipping code.
Capy vs Presto Voice
Presto Voice and Capy serve completely different domains: voice AI for QSR drive-thrus vs. multi-agent coding for dev teams. Pick Presto Voice if you run a chain of drive-thrus and want to automate orders with proven upselling ROI. Pick Capy if you lead a development team shipping code at scale and need parallel AI agents integrated with your workflow tools. There's no overlap — choose based on your industry.
Capy vs Truleo
These tools serve entirely different verticals. Truleo is purpose-built for law enforcement intelligence, connecting siloed data to automate lead generation and report writing. Capy is a multi-agent coding platform for development teams, offering parallel agents and model-agnostic orchestration. Your choice depends on your domain: policing or programming. Do not cross-shop them unless you need both, which is unlikely.
Alternatives to Capy
View allCursor
Cursor is your coding agent for building ambitious software — plan, build, test and ship with AI agents across IDE, CLI, Slack and cloud.
GitHub Copilot
GitHub Copilot is an AI coding agent that completes code, reviews pull requests, and runs Copilot, Claude, and Codex agents inside GitHub
Frequently Asked Questions
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