Where should our team start?

Start with the bottleneck that already hurts: a Sentry or CI trigger that should become a PR, a bounded feature or maintenance task, code review, database performance, documentation, or a shared environment for product feedback. CodePress can help configure the first workflow before you expand.

Can the whole team use it?

Yes. Engineers can delegate and review work, while founders, product managers, and designers can inspect live environments, leave feedback, or work through guarded flows without setting up the codebase locally.

Each run is isolated in its own sandbox, and credentials are stored encrypted and injected only for the run that needs them. You choose which repositories and tools an agent can reach, and engineers retain control over merge and production approval.

Which AI models can I use?

Pick the best model for the job — Claude, GPT, and more. Use CodePress-managed models at the provider’s token cost with no markup, or bring your own API key or supported ChatGPT or Claude subscription.

Built for real engineering work

Ship as fast as OpenAI and Anthropic.

CodePress gives your team the engineering system to match that pace: automated code review, agents that verify their own work, agent-ready codebases and infrastructure, and cloud orchestration that runs projects end to end.

Set up your codebase See it work

AgentsChatAppsMy WorkDesignIntegrationsPlatform MCPGeneral

CP-4182 · Production messaging

Re-architect message delivery at scale

CodePress Agent

WorkingGuidedI'm done

Re-architect message delivery at scale and open a verified PR.

I’ll trace the delivery and reconnect paths, then exercise them under production-like load.

Bash(rg "publish|replay|reconnect" apps/)

I found a replay gap during reconnect. I’ve repaired the ordering path and added coverage for concurrent sessions.

Running staging QA and independent merge judgment

The repaired build passed QA and review. PR #2184 is ready for your approval.

Describe what you'd like to do

GPT-5.6 Sol

Merge readiness

QA passed

Load and reconnect coverage passed on the repaired build.

Judge passed

Independent judge found no remaining blockers.

PR review passed

Review found no unresolved code or security issues.

Ready to merge

Every required check passed on the current head

80 → 400PRs per month on our own team

3 daysOne autonomous production rearchitecture

275Agent turns before ready to merge

Workflows

Start with the workflow your team needs now

CodePress is one engineering system with multiple ways in. Automate a bottleneck first, prove it in your stack, then expand from there.

Issue to tested PR

Delegate a feature, bug, migration, or infrastructure task. The agent implements it, tests the application, addresses review, and brings the PR back to your team.

Production signal to fix

Let a Sentry issue, failing CI run, database regression, or GitHub event start the right workflow and return an investigated fix instead of another alert.

Live app to approved change

Share a running development environment with engineers, founders, or designers. They can inspect the real product, leave feedback, and work with an agent without local setup.

See how the software factory completes long-running work

The System

Everything your AI engineer needs to do real work

Cloud execution, shared environments, company context, triggers, tools, QA, review, and approval workflows—already integrated for your team.

Reusable Engineering Workflows

Turn your team’s playbook into a system

Combine agents, company context, skills, tools, triggers, and approval gates into workflows the entire engineering team can run—not a setup that only works on one laptop.

Agent configuration panels for appearance, instructions, and triggers

Triggers and Collaboration

Start work where it already happens

Kick off engineering work from Slack, GitHub, Sentry, schedules, and connected tools. Follow progress and handle approvals without becoming the human message bus.

Slack, WhatsApp and GitHub icons orbiting a laptop

Model Independent

Bring the intelligence you already use

Route planning, implementation, review, and QA to the models that fit each job. Use CodePress-managed models or connect supported API keys and subscriptions.

A model picker listing Anthropic, OpenAI and OpenRouter models

Shared Cloud Environments

Give every task somewhere real to run

Agents work in isolated cloud environments where they can run the application, perform browser-driven QA, and share a live preview with the rest of the team.

A table of running agents with their owners and status

For the whole company

AI for the entire company

Engineering is where most teams start. CodePress is set up once for the company — then design, operations, support, and anyone else can build, change, and ship real software through the same system.

Internal tools and customer-facing apps

Admin panels, dashboards, customer portals, marketing pages. They come out of the same system, and they deploy to your own infrastructure or to hosting CodePress runs for you.

Design in the browser, not in a dev environment

Open the running product, point at what should change, and describe it in plain language. Nothing to install, no local setup, and no waiting for an engineer to free up.

Set it up once, everyone plugs in

Repositories, models, integrations, permissions, and approval rules are configured for the company instead of per laptop. Someone who joins on Monday is doing real work on Monday.

Manage your agents like a team

Give each agent an identity, the company data and tools it is allowed to reach, and the people who approve its work. You keep the scopes, the audit trail, and the final say.

What that adds up to

One engineer’s monthly output, before and after CodePress

Same engineer, same codebase. This is two years of their shipping volume, and what happened to it once CodePress became part of how they work.

Monthly engineering output for a single engineer from February 2024 to April 2026. Commits, merged pull requests, and lines changed stay low through 2024 and 2025, then climb steeply from late 2025 — from 324 commits and 22 merged pull requests in December 2025 to 1,823 commits and 453 merged pull requests in April 2026.

Monthly totals, December 2025 compared with April 2026.

Commits: 3241,823
Merged PRs: 22453
Lines added: 59.4k407.2k

Get your team set up

Bring agentic engineering to the whole team

Start with trigger-to-PR, autonomous development, code review, or a shared live environment. Expand only after the first workflow earns your team’s trust.

Connect the codebase

Keep your repositories, models, tools, and delivery rules.

Set it up

Choose the first workflow

We help configure one immediate need in your real stack.

Set it up

Pricing

Pay as you go or reserve an agent

No base subscription. Pay $4 per agent-hour on demand, or reserve a dedicated agent month to month.

On-demand

$4/agent-hour

Start on demand

Metered from runner start until stop and billed by the minute. Idle runners shut down automatically after 2–10 minutes; that idle tail is billable runtime.

No monthly compute commitment
Runner runtime billed by the minute
Scale up or down whenever you need

Reserved agent

$125/agent/mo

Start & Reserve

Reserve for one month and renew monthly.

One dedicated agent slot for the full term
Best value above roughly 31 hours per month
Additional concurrent agents run on demand

Pay for your own tokens

Use CodePress-managed models at the provider’s cost, or connect your own API key or supported model subscription.

CodePress-managed models

Provider token cost

Pay the model provider’s token cost with 0% CodePress markup. Model usage is separate from agent compute.

Bring your own

API key or subscription

Connect your own API key or a supported ChatGPT or Claude subscription. That model usage stays with your provider.

Help Center

FAQ

Have a question? We have answers.

Those tools provide strong coding agents for individual developers. CodePress is the engineering system around them: shared cloud environments, company context, triggers, team workflows, QA, review, and approval gates. Your team can keep using supported models and subscriptions through CodePress.

Reference

Every benchmark, broken onto the same five stages

Labs publish tables of scores with almost no explanation of what was measured. This is a plain-English breakdown of 78 of them — the tasks, the environment the model runs in, who grades the answer, and the fine print that decides whether two numbers can be compared at all.

01 · Source
Where the tasks came from
02 · Task
What one instance looks like
03 · Environment
What the model can do
04 · Judge
Who decides it is right
05 · Metric
What gets published

Browse all 78 benchmarks