$ introducing warp factories

Open infrastructure for cloud software factories

run fleets of coding agents across your SDLC: defined as code, on any model or harness, with humans at the checkpoints you choose.

request early accessread the docs

get up to $10,000 in free factory usage

>_[ fig. 1 — the factory ]⌗
livefactory.yaml
factory.yamlapplied to all 112 agents
# factory.yaml
schemaVersion: v1alpha1
name: acme-web
repositories:
  - owner: acme
    name: web
mcpServers:
  github:
    warpId: mcp_github
agentDefaults:
  model: claude-5-fable-high
trusted by 800k+ devs at
AsanaDockerGitHubVMwareAmplitudeTeamworksNVIDIARamp
early access

Request early access

Set up your first factory with early access.

get up to $10,000 in free factory usage

SDLC coverage

Beyond CI/CD to automating the whole SDLC

defined in code, built for scale, and easy to deploy.

>_[ fig. 2 · quickstart ]⌗
# set up your first factory from a skill
$ npx skills install warp-factory-setup

fetching warp-factory-setup...
installed ✓ — follow the prompts to configure your first factory
>_[ fig. 2 · factory.yaml ]⌗
# factory.yaml — factories as code
schemaVersion: v1alpha1
name: pr-review
repositories:
  - owner: acme
    name: web
agentDefaults:
  model: claude-5-fable-high

# agents/foreman/agent.md
agentType: FOREMAN

# agents/review/agent.md
agentType: REVIEW
model: glm-5.2-fireworks

# automations/on-pr/automation.md
agent: review
triggers:
  - provider: github
    event: pull_request_ready
>_[ fig. 2 · API / CLI / SDK / MCP ]⌗
# cli
$ warp agent run-cloud --environment ENV_ID \
    --prompt "fix LIN-482, low risk only"

# api
$ curl -X POST https://app.warp.dev/api/v1/agent/runs \
    -H "Authorization: Bearer $WARP_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{ "prompt": "fix LIN-482" }'
{ "run_id": "run_4026", "state": "QUEUED" }

# sdk (typescript)
const run = await client.agent.run({ prompt: 'fix LIN-482' });
console.log(run.run_id);

# mcp · warp-factory server
{ "tool": "send_task", "factory_uid": "fct_acme_web",
  "title": "Fix LIN-482", "note": "low risk only" }
>_[ fig. 2 · integrations ]⌗
>_[ fig. 2 · any surface ]⌗
>_[ fig. 2 · self-improvement ]⌗
30%+
automation coverage
PRs merged, zero edits
200k/day
agent runs
across all factories
-20%
cost per pr
measured over time
no lock-in

Open at every layer

>_[ fig. 3 · open at every layer ]⌗
layer 01

Any agent

warpclaude codecodexcursor

any MCP-capable coding agent.

layer 02

Any model

frontieropen-weight

frontier or open-weight, chosen per pipeline stage.

layer 03

Your compute or ours

warp cloudself-hosted

warp's cloud, or self-hosted in your own VPC.

layer 04

Data lives where you want

pluggablezero-retentionyour VPC

you own and store what your factory produces.

quality loop

Baked-in measurement and self-improvement

evals, benchmarks, and self-improvement loops drive measurable gains.

>_[ fig. 4 · quality loop · sample run ]⌗

evals on your own work

pass96% 
100%50%0%apr 01may 01jun 01jul 01
8/13 07:03fail0.4$0.39
8/13 07:02pass0.8$0.53
8/13 07:02pass0.8$0.46

benchmarks across models

best1.00x 
1.0x0.5x0xapr 01may 01jun 01jul 01
Claude Fable 5pass1.00x$0.53
GPT-5.6 Solpass0.94x$0.41
Gemini 3.6 Flashfail0.89x$0.32

self-improvement loops

auto-fix+3.7 
+4+20apr 01may 01jun 01jul 01
memory updatedpass+0.2#4021
prompts tunedpass+3.2%#4022
regression caughtfail-0.4#4023
"warp factories drove our cost per agent pr down by 30%."
— vp engineering, series c infrastructure company
governance

Control your coding agent chaos

continuous improvement, better governance and security, by default.

>_[ fig. 5 · control plane ]⌗

increase agent roi · cost per pr

$—
computeplatforminference

one control plane for every agent

resolve merge conflicts on server pr 14090fable 5orchestrationfnow
refactor auth middleware for session reuseopus 4.8claude codez14m
investigate child process kill issuefable 5slackj37m
investigate usize panic in offset codegpt-5.6 solslackf1h
fix desktop tab crashgemini 3.6slackj2h
add rate limiting to public api endpointsgpt-5.3codexe4h

agents prove their work with computer use

every factory agent captures a screenshot or video so you can verify its work before shipping a PR.

Activations dropped on the contact sales page this week. Can you investigate and fix the culprit?

On it — opening the browser to test the flow myself...

warp.dev/contact-sales
Bring Warp
to your team

Enter your work email and answer one question — we'll get you to the right person on our team.

Book a demoWork Email*you@company.comCONTINUE

Trusted by over 800,000 developers and thousands of engineering teams at leading companies

Found it — the testimonials section was hidden by collapsing divs. Patched, PR #436 is up for review.

built to scale

The factory infrastructure you'd build yourself

available now, so your eng time goes into your product, not the plumbing. tick a box to clear the desk (drag the pile around on desktop).

TO_DO
Slack and Teams event webhooks
TO_DO
Jira and Linear issue webhooks
TO_DO
GitHub and GitLab push/PR webhooks
TO_DO
webhook signature verification
TO_DO
cron scheduler for triggered runs
TO_DO
event-to-task deduplication
TO_DO
per-run sandbox provisioning
TO_DO
Docker- or Kubernetes-backed compute
TO_DO
environment image build and caching
TO_DO
cold-start latency for new runs
TO_DO
sandbox teardown and cleanup
TO_DO
multi-harness process management
TO_DO
Claude Code, Codex, Cursor, and Warp support
TO_DO
model routing per pipeline stage
TO_DO
MCP server connection management
TO_DO
CLI invocation inside the sandbox
TO_DO
source-forge integration: GitHub, GitLab
TO_DO
non-human identity per agent
TO_DO
credential and token handling
TO_DO
fine-grained permissions per agent
TO_DO
secrets storage and rotation
TO_DO
quotas and concurrency limits
TO_DO
network egress policy enforcement
TO_DO
run lifecycle state machine
TO_DO
durable execution across long-running steps
TO_DO
retries and idempotency
TO_DO
multi-agent handoff between stages
TO_DO
workflow versioning
TO_DO
thread and run continuity
TO_DO
real-time log and event streaming
TO_DO
run history and audit trail storage
TO_DO
audit trail per tool call
TO_DO
live session attach for steering
TO_DO
failure detection and alert routing
TO_DO
approval-gate enforcement before merge
TO_DO
checkpoint pause/resume state
TO_DO
evals and scoring pipeline
TO_DO
artifact and output storage
TO_DO
benchmark regression detection
TO_DO
…and everything you haven’t hit yet

Frequently asked questions

a factory is a fleet of agents wired to your SDLC — triggered by an issue, a slack message, or a schedule, and coordinated by warp factories from triage through review to a mergeable PR.

as code, in a factory.yaml + supporting agent files. define triggers, agents, models, and approval gates — check it into your repo like any other config.

no. bring your own model or harness (e.g. claude code or codex) — warp factories works with whatever your team prefers and helps identify the most cost-effective configuration over time.

most orgs start around 20-30% of PRs fully automated, starting with simple tasks. over time this goes up as models improve and your factory self-improves.

wherever you choose — warp's cloud, or fully self-hosted inside your own VPC, under your existing retention and compliance rules.

humans put work into the factory via your existing tools like slack, teams, linear, jira, github and gitlab. at any time you can iterate on that work locally and the factory agents loop you in proactively when they need help.

yes — most teams start with a single low-risk workflow, like dependency bumps or flaky test triage, before expanding coverage.

usage-based, priced per agent run. qualifying orgs get $10k of factory usage during closed early access.