Spring 2026

Replicas

Run any coding agent harness in the cloud

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What they do

Cloud coding agents that take tickets and fix CI failures

How close to us

4, very close

Delegated coding agents that fix CI and act on reviews; team size not stated so not rated 5.

Wedges

  • W1 Trusted 24/7 coding for small startup teams direct
  • W2 Seeing whether your AI agents actually produce adjacent
  • W8 The other 90% of an AI skill adjacent
L1 Company brain (knows the organisation) 2, clear
L2 Agent team (runs agents, tools, approvals) 2, clear
L3 Does and learns (finishes, checks, measures, updates the brain) 2, clear

Who buys

Firm size
sme
Buyer type
developer
Role
head of engineering or CTO at a startup
UK
not known
Their own description (from YC)

Replicas is a cloud coding agent that allows you to delegate tasks to Claude Code or Codex from the app, Slack, Linear, and more. Each task works inside of a sandboxed virtual machine, allowing teams to heavily parallelize work and burn through tickets.

Replicas agents output higher quality PRs via feedback loops - each task automatically is notified and fixes CI failures and reads all code reviews. With Environments, engineering teams can configure agents to run their entire codebase locally, verify its work, and provide preview URLs.

Full teardown

Replicas (YC Spring 2026): teardown vs XMarts

In one line: Replicas runs Claude Code, Codex and other coding agents in cloud machines. Engineers hand them tickets from Slack, Linear or GitHub and get pull requests back [site] [yc]. Threat: 3 because it is the same buyer and the same problem as W1 (24/7 coding for startups), it raised a $4M seed on 13 Oct 2026, and it has named customers [blog] [site].

1. Fact file

Item Finding Source
What they say Home page: "The Cloud Coding Agent". YC line: "Run any coding agent harness in the cloud". [site] [yc]
Founded, team, funding Founded 2026. YC page lists 2 people, in person in San Francisco. $4M seed announced 13 Oct 2026: Y Combinator, Flex Capital, Founders' Co-op, plus Pioneer Fund, Robinhood Ventures and others. Angels include Paul Graham. [yc] [blog]
Founders Connor Loi (CEO; ex Helicone, TD Bank, Cohere) and Saai Arora (CTO; ex General Context, Shopify, IBM, Autotrader; Waterloo). Two Helicone founders are seed angels. What YC likely saw: a repeat dev-tools founder who shipped V1 within weeks of the January plan (inference). [yc] [blog]
Customers Logos on home page: Mintlify, Knowunity, Vela, Composio, Parrot, Cargo, Loop AI, Moda, Dart, Chronicle Labs, Sorce, Taiga, Trellis, Hyperspell, Totalis, Utility Profit. Five case studies (Knowunity, Parrot, Moda, Utility Profit, Vela). [site]
Traction (they claim) Customers ship over 30% of pull requests with Replicas. Agent usage passed 1M minutes in May, 5M in September, nearly 7M three weeks later. Agents ran in over 1M workspaces. 20+ YC startups. Customer count and revenue not disclosed. [site] [blog] [yc]
Price Public. Developer $50 a month, Team from $200 a month (usage included equals the fee), Enterprise custom. Workspace minutes metered from $0.004 to $0.016 a minute included, and $0.006 to $0.024 a minute overage. $100 free to start, no card. A June 2026 third-party review listed $120 and $300 per seat instead, so the model changed (inference: seats to usage). [site] [press]
Path 5 Jan 2026 mission post. 18 Mar 2026 V1 launch. Spring 2026 YC batch. 12 Aug 2026 brand refresh "for the next step" (a move from personal tool to whole-team platform). 13 Oct 2026 seed. No rename found. [blog]

2. Their proposition, reverse engineered

Part What they do
Who buys Engineering teams that already use Slack, Linear and GitHub. Early customers are startups [site]. Enterprise page adds SCIM, audit log, SOC 2 Type I, single-tenant or self-hosted options [site].
The pain A ticket queue no one has time to clear. Local agents are tied to one laptop. "Developer machines and cloud machines basically have the same environment now" (Parrot CTO) [site].
The promise Hand over tickets, review pull requests, ship more in parallel. The gap between an agent and an engineer is the environment, not the model (their claim) [blog].
How it works Each task gets its own Linux VM with the code, services and tools running. The agent writes, runs and checks the work, reads CI failures and code reviews, and opens a PR. Triggers: Slack, Linear, GitHub, GitLab, schedules, webhooks, API [site] [yc].
The output Pull requests, replies, or screen recordings to merge or send back [site].
Proof style Usage and share counts: minutes, workspaces, "over 30% of PRs", "doubled velocity", "3x faster reviews" [site] [blog]. No money result. Their own backlog test is more honest: 50% of PRs mergeable first time, 12% wrong or abandoned (5% plus 7%) [blog].
Who sells it Self-serve sign-up with free credit, a demo booking link, and enterprise contact sales. Founders answer email. Hiring a "Founding Growth" role, so sales is not yet built out [site] [jobs].
Price model Monthly fee that equals included usage, then per-minute overage by machine size. Not per seat [site].
The story Agents should run in the cloud as "a team member, and not a ticket clearing utility" [blog]. Do not build your own harness: be "harness agnostic" and win on the computer the agent gets [blog].

3. Positioning

  • Category they claim: Cloud coding agent; background coding agents; harness-agnostic cloud infrastructure for agents [site] [blog].
  • Category a YC partner would put them in: developer tools, background coding agents, in a crowded category (inference).
  • What they want to be known for: the best computer for any coding agent, wired into the tools a team already uses.
  • Where they sit on the map: layers L1/L2/L3 = 2/2/2, buyer = sme (named customers are startups), developer.

4. Three layers vs ours

Layer Them (score + evidence) Us (Marton's logs show)
L1 Company brain 2. Long-term memory shipped Sep 2026: team-level decisions and procedures stored as Markdown files, built by background workers from past sessions, read by later agents. Early test on five open-source repos, not full results [blog]. Coding work only, no sales, finance or people data. A vault of about 1,400 pages, 112 call transcripts and about 8,700 customer quotes, searchable by meaning. Learns from calls and checked work.
L2 Agent orchestration 2. Many agents in parallel, each in its own VM; four harnesses; automatic fallback to another provider; fork a task from Claude to Codex; role-based access; human review of PRs [site] [blog]. About 5,600 jobs run by agents from three makers, each checked by a different AI, then automatic tests.
L3 Execution and learning 2. Does the work, fixes CI, reads reviews, verifies in a running app [yc] [blog]. Analytics measures "usage, not outcomes": tokens, harness, who triggered [blog]. Memory learns from sessions, not from measured results. Not a 3. Every failure gets a record and an automatic check (about 160). Offer feedback lines fell from 44 to 18 as rules were added. Only about 15% of jobs reached the main code, and merges were unreliable in October.

5. Same and different

Topic Same or different Them Us
Core belief Same Agents should work in the background while people sleep or do other work [blog]. Same (brief: 24/7 agent work).
Buyer Same Startup engineering teams [site]. W1: Series A/B startups, up to about 30 engineers (brief).
Engines Same Claude Code, Codex, Cursor, Opencode [site]. Marton's logs show jobs split across Codex, Claude and Gemini.
Who checks the work Different The human reviews the PR. They used a separate review tool (Greptile) in their own test [blog]. Marton's logs show every job checked by a different AI before automatic tests.
Merge decision Different A May 2026 post by them says nothing in their stack then knew which PRs to auto-merge and which to escalate, so a human decides [blog]. Marton's logs show a gated merge path (368 gate checks), but it was fragile in October.
Where it runs Different Cloud VMs they host; enterprise can self-host [site]. Three Macs owned by one person. No outside user yet.
Learning Different Memory from session history [blog]. Rules added after every failure and every piece of feedback.
Proof Different Minutes and PR share, self-reported [blog]. Our own numbers, one user, no customer.
Size and speed Different Live product, 16 named logos, $4M seed [site] [blog]. Prototype on our own business.
Price Different Public, from $50 a month [site]. Not set.

6. Wedge overlap

Wedge Fit Why
W1 Trusted 24/7 coding for small startup teams direct Same buyer, same promise, live and funded [site] [blog]. Their gap is "trusted": the human still decides what merges [blog].
W2 Seeing whether your AI agents actually produce adjacent Their analytics shows usage by person, harness and model, not outcomes [blog]. They could extend it. W2 would need to show output and silent failure, across more than their own agents.
W8 The other 90% of an AI skill adjacent Skills are a setting inside an environment [blog]. Memory is compiled from sessions [blog]. They do not claim skill upkeep from accepted or rejected work (UNVERIFIED that none exists). Nessie is still the closer rival (brief).

7. Whitespace (where they stop)

  • Layer gap: they stop at a PR a human must still approve and merge [blog]. Owning the merge decision means showing a gated path that merges without a person on the routine cases. We would need a run of real merges with a low bad-merge rate on a customer repo.
  • Number gap: their proof is usage and share, and their one honest test shows 12% of PRs wrong or abandoned [blog]. We would need a before/after number a head of engineering would sign: merged PRs per week and rework, over 4+ weeks.
  • Buyer gap (weak): they already sell to startups of all sizes and are opening an enterprise tier [site]. Utility Profit's case study title says "all departments" (page not readable, UNVERIFIED), so non-engineers may come next. No true buyer gap found for W1.
  • Price gap (none): from $50 a month with $100 free [site]. We cannot undercut them.
  • Speed gap (none): self-serve sign-up and free credit; first value in minutes [site].

8. For the YC application

  • Proven today: Marton's logs show his system runs about 5,600 agent jobs across three makers' engines. Each job is built in its own copy of the code and checked by a different AI before automatic tests. About 160 failures each got a record and an automatic check. A May 2026 Replicas post says nothing in its stack then knew which PRs to auto-merge and which to escalate [blog]; they may have changed this since.
  • Draft answer to "how are you different from Replicas?": DRAFT. "Replicas gives agents a computer and hands you pull requests to review. We care about what happens after: checking the work with a different AI and deciding what is safe to merge. We have run that loop on our own business for five months." Founders say it their own way.
  • Future claim (needs proof): "Teams merge agent work without reading every PR, and we show the bad-merge rate." Needs one customer repo run for 4+ weeks with a before/after number (merged PRs, rework, bad merges).
  • What we understand that they don't: the hard part of 24/7 agent coding is trusting the output, not producing it. needs proof on a customer; Marton's logs show the failure history on his own system, including that only about 15% of jobs reached main.
  • Copyability (M3): easy. They already run several engines, switch an agent from Claude to Codex mid-task [blog], and used a separate review tool in their own test [blog]. A second-engine review step is a feature for them, and it fits their buyer (inference). Memory, analytics and a "manager" direction show they are moving toward the same loop [blog]. The deeper part (a failure taxonomy and a gated merge path built from real incidents) would take them longer, but nothing public shows they want it yet. Not hard, so M3 cannot score above 3 against Replicas.
  • Why now (M4): None found in this teardown. Do not use "cloud agents became possible" or the Stripe and Ramp background-agent posts: they help Replicas as much as us.
  • Do not claim: "trusted" or "safe to merge" as a result we have already delivered (only about 15% of jobs reached main, and landing was fragile in October). "We have customers" or "we are cheaper". "Replicas cannot check work": they used a review tool, and they may add one. Any claim about their revenue.

9. Ideas worth taking

  1. Publish an honest outcome table on your own backlog (50% mergeable first time, 12% wrong or abandoned). It builds trust, and we can copy the shape using our own jobs.
  2. Make setup a saved, shared environment per customer, so the second task starts warm. It cuts time to first value.
  3. Start the trigger where the work already lives (Slack, Linear, a PR comment), not in a new app.

10. Unknowns

  • Real revenue, number of paying customers and how much of the 7M minutes is paid or free credit. Ask a customer or read their next update.
  • Whether they plan a merge gate or an outcome score. Watch their blog and changelog for "auto-merge", "review" or "quality".
  • Where their buyers are based. No UK signal found. Check customer pages and careers.
  • Utility Profit's case study and several blog pages did not load in full; the memory and analytics posts were read in summary only. Re-read the full text before quoting.
  • A "V3" label appeared in a search result but not on their site (UNVERIFIED).
  • Team size: YC page says 2; a June 2026 review said 1. Newer hires are possible.

Sources

Technical detail
Slug
replicas
Lens hash
179096c479
Pass
deep
Stale
no