Open Source Devin Alternatives

OpenHands ships Agent Canvas, a self-hosted developer control center that turns coding agents into an always-on engineering team. It handles both the conversations you start yourself and the everyday chores you would rather delegate, such as publishing a report to Slack or decomposing a GitHub issue into tasks.

It runs on your own machine by default and connects to agent backends elsewhere, whether those are Docker containers, virtual machines or servers inside company infrastructure. Underneath sits the OpenHands Agent Server, a REST API for running several agents on one host, usually paired with an automation service that decides when work runs.

The control center is built around a few choices you keep open.

• Any agent: OpenHands, Claude Code, Codex, Gemini or anything speaking the Agent Client Protocol.
• Backend switching: move between local, remote and cloud agents from the same frontend.
• Automations: schedule runs or trigger them from webhook events.
• Integrations: connect workflows to Slack, GitHub, Linear, Notion and others.
• Bring your own model: point the agents at whichever LLM you prefer.

Installation is an npm package needing Node 22.12 or later, a Docker image with your projects directory mounted, or a build from source. Running without the Docker sandbox gives the agent full access to your filesystem, so the self-hosting notes on security hardening are worth reading first. The project is in beta.

Warp began as a terminal and now describes itself as an agentic development environment. You use its built-in coding agent, or point it at a CLI agent you already run such as Claude Code, Codex or Gemini CLI.

The client codebase lives in this repository and builds from source through three scripts, one for platform setup, one to build and run, and one that formats, lints and tests before you submit. OpenAI is the founding sponsor of the open source repository, and the agentic management workflows run on GPT models.

Development of Warp itself happens largely in the open, and much of it is done by agents.

• Warp Factories: infrastructure defined in code for cloud software factories, with evals built in.
• Contribution dashboard: watch agents triage issues, write specs, implement changes and review pull requests.
• Readiness labels: maintainers mark issues ready to spec or ready to implement for contributors.
• Agent sessions: click into an active session running in a web-compiled Warp terminal.
• Dual licensing: the UI crates are MIT while the rest of the repository is AGPL v3.

Downloads cover platform-specific installs and a preview build carries experimental features. The project credits dependencies including Tokio, NuShell, Alacritty, Hyper and the Fig completion specs, and contributors gather with maintainers in a public Slack channel.

SWE-agent hands a language model a set of tools and lets it work. Point it at a real GitHub repository and it attempts the issue on its own, and the same machinery has been aimed at offensive cybersecurity challenges and at arbitrary custom tasks.

The project comes from researchers at Princeton University and Stanford University, and the design reflects that. Behavior is governed by one YAML file rather than scattered code, the agent is left maximal agency instead of being railroaded down a fixed script, and the codebase stays simple and hackable so an experiment is cheap to run.

Several points matter before you pick it up.

• Superseded by a smaller sibling: most development effort has moved to mini-swe-agent, which matches the performance while being much simpler, and the maintainers recommend it going forward.
• Model agnostic: bring the model you prefer, with GPT-4o and Claude Sonnet 4 named as examples.
• Benchmark record: it holds state of the art results on SWE-bench among open source projects.
• EnIGMA mode: an offensive security mode solves capture the flag challenges, currently requiring version 0.7 while it is brought up to date.
• Try it first: a Codespaces launch runs the agent in your browser with no local installation.

Documentation covers installation from source, a command line hello world and batch mode for benchmarking against SWE-bench. Given the maintainers' own advice, treat this repository as the research reference and the fuller feature set, and reach for the smaller agent when you simply want results.

Roomote is a coding agent you deploy and own rather than a plugin inside an editor. A task handed to it in Slack, Teams, Telegram, Discord or the web interface leads to a cloned repository in a sandbox, code written, tests run and a pull request opened for review.

It is a full-stack application, not a wrapper. Every task gets its own throwaway sandbox on Modal, E2B, Daytona, Blaxel or local Docker, so nothing touches your machine. Models come from a connected ChatGPT subscription or your own API keys, and source control can be GitHub, GitLab, Gitea, Azure DevOps or Bitbucket Cloud.

The work it takes on is the kind that interrupts a main project.

• Bug fixes: a stack trace or a Sentry link is reproduced in the sandbox before a fix is written.
• Codebase questions: it reads the code to answer how authentication works or where the billing logic lives.
• Chores: dependency upgrades, linter fixes, config changes, migration files and boilerplate.
• Small features: it writes the change, runs the app and attaches a screenshot and a preview link.
• Issue triage: connected to Linear, Jira or GitHub Issues, it reads new tickets and asks questions.
• Audit trail: every run logs the model used, the tools called and the code written.

Deployment runs from one-click templates on Railway and Render to an install script on a fresh Ubuntu or Debian machine. The source is available to read and fork, free for up to ten users, with a license sold beyond that.