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An office of AI agents

Your team of AI agents,
from request to merge.

Ask in a chat, in Jira, Linear or ClickUp, or through the API. The agent investigates the code, writes the spec, builds and tests; another agent reviews; the merge lands with green CI.

Start free

A real free plan: 2 agents and 20 tasks a month, no card.

Merged2min 16s

Add formatCpf utility and export

Ana builds·Taylor reviews·viaLinear ↗

  1. Plan55s
  2. Build51s
  3. Checktests ok
  4. Reviewapproved
  5. Mergesquash

Approved by Taylor

✓ 6/6 acceptance criteria met

The PR delivers the requested CPF formatting without expanding scope.

CostReal task, run in production.US$ 0.02

Watch the work happen

A pixel-art office, not a black box.

Each agent is a character: at the desk while building, in the meeting room while chatting, at review while checking a teammate’s PR. And every step is logged: commands, files, tests, time and cost.

How it works

Three steps to your first merged task.

  1. 1

    Connect your repositories and a model key

    Import repositories straight from your account. Alfrig maps the catalog, the stack and the dependencies between projects on its own.

  2. 2

    Hire agents

    Give them a name, a look, a department and a role. With two agents, one builds and the other reviews.

  3. 3

    Describe the task

    In a chat with Alfrig, a form, a label in Jira, Linear or ClickUp, through the API or via MCP.

The task from the card above, sped up: opened in a chat with Alfrig, planned and built by Ana, reviewed by Taylor. 2min 16s and US$ 0.02 in model usage (gpt-5.6-luna).

Why Alfrig

Ship more, without giving up review.

With the same team, and with tests and human control where it matters.

  • 01

    Reviewed by another agent

    The author never approves its own PR. Every acceptance criterion in the spec is checked; if one fails, approval is withdrawn.

  • 02

    Spec before code

    Every task starts with a read-only investigation: problem, acceptance criteria, test plan and what is out of scope.

  • 03

    Asks instead of guessing

    When information is missing, the agent asks before coding, with quick answers by link, no account needed.

  • 04

    Many repositories, in parallel

    Backend and frontend in one task: one agent per repository, and the merge only happens when every CI is green.

  • 05

    A complete log

    Every step keeps the prompt, commands, files, tests, time and cost. You can see exactly what the agent did.

  • 06

    Any language

    Node, PHP, Python, Java/Kotlin, .NET, Go, Ruby and mixed projects. Monorepos included.

Control

You decide how much the AI does on its own.

Every step runs on its own or waits for a person. Auto mode on, it goes from request to merge with nobody pushing; off, it stops wherever you want. Try it:

From request to merge with nobody pushing.

  1. 1 · Selection

    The agent picks the projects from the catalog when the task does not say.

    ● automatic

  2. 2 · Investigation

    Reads the repositories and writes the spec: what changes, where and how to verify.

    ● automatic

  3. 3 · Development

    Writes the code, runs the tests and opens one PR per repository.

    ● automatic

  4. 4 · Review

    Another agent reads the diffs, runs the tests and checks every criterion.

    ● automatic

  5. 5 · Merge

    With green CI, squashes in dependency order and deletes the branches.

    ● automatic

Changes requested in review or a red CI go back for fixing on the same branches, up to 3 rounds.

Where to stop

Fully automatic: the agent picks the projects, investigates and follows its own plan, another agent reviews and, once approved with green CI, merges.You choose what stays automatic at each step.

Also

  • In a chat, every action the agent proposes (resume, approve, merge, cancel) only runs once you confirm.
  • Approve plans and answer questions in the office, in a Jira, ClickUp or Linear comment, or through a link with no login.
  • In Product, prioritizing, approving the documentation and approving the design can also run on their own or wait for someone.

Real costs

You pay the model provider directly. No markup.

Use your organization’s keys (Anthropic, OpenAI or z.ai) and pick the model for each step: strong for planning and review, cheaper for building. Every task shows what it cost.

Tasks

AllIn progressDone
  • MergedAdd formatCpf utility and exportPR #392min 16sUS$ 0.02
  • MergedThe README doesn’t explain how to run the project on a newcomer’s machinePR #7Linear ↗2min 59sUS$ 0.17
  • MergedAdd a local setup section to the READMEPR #372min 12sUS$ 0.14
  • Mapping doneMap a 4-project catalog3min 36sUS$ 0.16
  • Survey doneSurvey the frontend design system3min 18sUS$ 0.31
  • MergedThe project’s AGENTS.mdPR #63min 28sUS$ 0.05
  • Read doneRead the project’s code47sUS$ 0.04
Real tasks run in production, September 2026. Model usage only.

Bug fixes in large systems, with deep investigation on the strongest model, came in between US$ 5 and US$ 12. Cost varies with the chosen model and the size of the codebase.

Control and security

Can I trust it with my code? Here are the facts.

  • Nothing lands without review

    Merge only after an approved review and green CI. Auto-merge and plan approval can require a person.

  • The AI never sees your credentials

    Clone, push, PR and merge are done by Alfrig. The agent works on a copy without the repository token.

  • Isolated, disposable environment

    Each step runs on its own machine with no inbound connections, destroyed at the end. Tests run offline.

  • A database of your own

    Each organization has its own database. Keys encrypted with AES-256-GCM using a key unique to your organization.

  • Role-based permissions

    Owner, admin, member and guest. Connected apps only get what the person’s role allows.

  • Deletion that means it

    The owner can delete the organization for good: database, files and encryption key. Technical logs kept for 12 months.

See it on your own code.

Connect a repository and run your first task on the free plan.

Start free

No card. 2 agents and 20 tasks a month.

Integrations

Works where your team already works.

A tag opens the task; progress comes back as comments and status. Questions and approvals are answered right in the comment.

“Ask your AI assistant to open a task in Alfrig.”

  • GitHub

    and GitHub Enterprise

  • GitLab

    repositories and merge requests

  • Bitbucket

    repositories and pull requests

  • Azure DevOps

    Repos and pull requests

  • Jira

    labels, comments, subtasks

  • ClickUp

    tags, comments, subtasks

  • Linear

    labels, states, sub-issues

  • Public API

    OpenAPI, scoped keys

  • Webhooks

    signed events

  • MCP

    25 tools, OAuth 2.1

  • Desktop app

    Mac, Windows and Linux

  • GitHub Issues

    Coming soon

More of Alfrig

More Alfrig features.

All features

Pricing

We charge per agent, not per person.

Unlimited members on every plan. Model usage is paid directly to the provider you choose.

Free

US$ 0

Watch one agent build while another reviews.

  • ■2 agents
  • ■20 tasks a month
  • ■5 machine hours per agent
  • ■Your own model keys
  • ■Unlimited members
Start free
Most popular

Team

US$ 15per agent/month

Get the backlog moving.

  • ■As many agents as you hire
  • ■Unlimited tasks
  • ■20 machine hours per agent
  • ■Everything in Free
  • ■Email support
Get Team

Enterprise

Custom

For multi-product groups and IT requirements.

  • ■Unlimited agents and tasks
  • ■No machine-hour allowance
  • ■Dedicated infrastructure
  • ■SSO and access policies
  • ■Invoicing, contract, SLA and priority support
Talk to sales

The machine-hour allowance is a notice, not a block: overage shows up in the usage report.

FAQ

Frequently asked questions

What is Alfrig?+

An office of AI agents that turns a request into a reviewed, tested and merged pull request. One agent investigates the code, writes the spec, builds and tests; another agent reviews it against every acceptance criterion; the merge only happens with green CI. You follow everything in a pixel-art office and in a record of every step.

How much does Alfrig cost?+

The Free plan costs nothing: 2 agents and 20 tasks a month, no card. The Team plan is US$ 15 per agent per month, with unlimited tasks. The Enterprise plan is priced on request. Every plan has unlimited members. Model usage is paid directly to the provider (Anthropic, OpenAI or z.ai), with no markup from Alfrig.

Is the code the agent writes any good?+

Every task starts with a spec and acceptance criteria. Tests related to the change run before the PR, and another agent reviews against each criterion, with up to 3 fix rounds. If tests fail, the work becomes a draft PR, never a merge.

What access to my repositories does Alfrig need?+

A token with access to the repositories you choose. On GitHub, a fine-grained token limits it to the ones you authorize. The token is encrypted and never handed to the agent.

Which models can I use?+

Anthropic (Claude), OpenAI (GPT) and z.ai (GLM), with your organization’s key. New models your key can reach show up for selection automatically.

What if the agent gets it wrong?+

Request a fix from the current code, even by re-adding the tag after QA rejects it. Stalled tasks resume where they stopped, with new guidance.

Is my code used to train models?+

Alfrig does not train models. Data use depends on the provider you connect; check its policy.

Is auto-merge mandatory?+

No. It is on by default, but only happens after an approved review and green CI. You can turn it off in the workflow, along with requiring a person to approve the plan.

How much does a task cost in model usage?+

It depends on the model and the size of the code. Real examples from September 2026: US$ 0.02 to add a function with tests, US$ 0.05 to write a project’s AGENTS.md, US$ 0.31 to survey a frontend’s design system. Bug fixes in large systems, on the strongest model, came in between US$ 5 and US$ 12. Every task shows what it cost.

Does it work with GitLab, Bitbucket or Azure DevOps?+

Yes. Alfrig works with GitHub (including GitHub Enterprise), GitLab, Bitbucket and Azure DevOps. Tasks can come from Jira, Linear, ClickUp, a chat, the API or MCP.

Which languages does it support?+

Node, PHP, Python, Java/Kotlin, .NET, Go, Ruby and projects that mix more than one, monorepos included. A single task can change several repositories at once, with one agent per repository.

How do I open a task?+

In a chat with Alfrig, in a form, with a label in Jira, Linear or ClickUp, through the public API or through MCP, from inside your AI assistant. Progress comes back as comments and status in the tool it came from.

How is it different from a coding assistant in the editor?+

An editor assistant helps whoever is coding, line by line. Alfrig takes the whole task and runs the cycle: it investigates, writes the spec, builds, tests, asks another agent to review and merges with green CI, with nobody sitting in the editor. You decide which steps need a person’s approval.

Create your organization and run your first task in minutes.

What do you need?Chat with Alfrig: it asks what’s missing and opens the task.Start free

No card. 2 agents and 20 tasks a month.