Devin made the idea concrete: hand an agent a ticket, get a pull request back. Since then most AI coding vendors have shipped something that does the same, so "is there a Devin alternative" has an easy answer. The useful question is which one fits how your team ships software. We build one of them, so read this with that in mind; we have tried to describe the others the way their own users would.
Four questions that separate them
Every agent on this list can write code that compiles. What differs is everything around the code:
- Who decides what gets built? Does the agent start from the ticket text, or is there a written plan someone approves first?
- Where does the code run? In the vendor's cloud, in your CI, or on machines you own?
- Which model writes it? One vendor's own agent, or a choice you can change when a better model ships?
- How do you pay? Per unit of compute, per seat, or a flat plan, and can you see the cost of a task before it runs?
The alternatives
GitHub Copilot coding agent. Assign an issue to Copilot and it works in a GitHub Actions environment until there is a pull request. The obvious choice if everything you do already lives in GitHub and you want the least new tooling.
Cursor background agents. Started by a developer from the editor, running in the cloud while they do something else. Strong when the work begins with an engineer who already knows what they want.
OpenAI Codex and Claude Code. Both are coding agents you can run in the cloud, in CI or in a terminal. They are excellent at the coding itself and leave the process around it, approvals, cost limits and audit, to you.
Google Jules and Factory. Asynchronous agents that pick up tasks and return pull requests, each with its own take on how much the agent plans before it codes.
CodeLynq. Ours. It puts a spec your team approves between the request and the code, lets you choose Claude Code, Codex or Grok as the agent that writes it, runs your tests, a self-review, a security scan and your architecture rules before the pull request opens, and can run on your own hardware. It is priced as a flat monthly plan, with the cost of every run shown before you approve it. The full comparison is on CodeLynq vs Devin.
Where autonomy stops being the point
Devin's pitch is that it needs little from you along the way. For a single developer clearing small tasks, that is the right goal. For a team, the expensive failure is not an agent that asks too many questions, it is a confident pull request that solves the wrong problem, found in review after the work is done.
That is why we went the other way and made the plan the checkpoint: the agent writes a spec against your codebase, open questions are asked instead of guessed, and nothing runs until someone approves it. We wrote up how that works in spec-driven development.
How to choose
- Everything in GitHub and you want minimum change: Copilot coding agent.
- Work starts with an engineer in their editor: Cursor.
- You want the best raw coding agent and will build the process yourself: Codex or Claude Code.
- Your team wants to approve what gets built, choose the model, keep code on its own machines or work under EU data processing: CodeLynq.
Whichever you shortlist, test it on a real task from your own backlog, not a demo repository. That is the idea behind our Backlog Challenge: send one task, and review the pull request we open on your code.