Devin AI Alternatives: 10 Coding Agents Compared

A VS Code fork rebuilt around AI. Cursor embeds AI into every part of the editing experience — tab completion that predicts multi-line changes, inline chat that understands your entire codebase, and an agent mode that can create files, run terminal commands, and make multi-file edits. The key difference from Devin is that Cursor works inside your editor, not in a separate environment. You see every change as it happens and can intervene instantly.

Best for: Developers who want AI deeply integrated into their editor workflow with real-time collaboration rather than asynchronous task completion.

vs Devin: More hands-on, faster feedback loop, significantly cheaper. Less autonomous — you're pair programming with AI rather than delegating.

The most widely adopted AI coding tool, embedded directly in VS Code, JetBrains, and other editors. Copilot started as an autocomplete engine but has expanded significantly — Copilot Chat provides contextual Q&A, and the agent mode can handle multi-file edits, run terminal commands, and iterate on code autonomously. The tight GitHub integration means it understands your pull requests, issues, and repository context natively.

Best for: Teams already on GitHub who want AI coding assistance that integrates seamlessly with their existing workflow and tools.

vs Devin: Less autonomous but more integrated. Works inside your existing editor and GitHub workflow rather than operating in a separate sandbox.

Anthropic's command-line coding agent. Claude Code runs in your terminal and operates directly on your local codebase — reading files, writing code, running commands, and fixing errors in a conversational loop. It understands project structure, can navigate large codebases, and handles multi-file changes naturally. Because it operates through the Anthropic API, you pay per usage rather than a flat subscription, which can be cheaper or more expensive depending on volume.

Best for: Developers who prefer terminal workflows, want agentic coding on their local machine, and are comfortable with usage-based pricing through the Anthropic API.

vs Devin: Runs locally in your terminal rather than a cloud sandbox. More interactive — you're in the loop at every step. No monthly subscription but API costs can add up with heavy use.

Built by Codeium, Windsurf is an AI-native code editor that combines real-time suggestions with an agentic "Cascade" mode that can plan and execute multi-step coding tasks. The editor tracks your actions and context continuously, so the AI understands not just your code but your intent based on recent edits. The free tier includes meaningful AI functionality, making it one of the most accessible entry points for AI-assisted coding.

Best for: Developers who want an AI-first editor with both autocomplete and agent capabilities at a lower price point than Cursor or Devin.

vs Devin: Editor-based like Cursor but with a more aggressive free tier. Less autonomous than Devin but more interactive and significantly cheaper.

An open-source terminal-based AI pair programmer that edits code directly in your local git repository. Aider connects to any LLM provider (OpenAI, Anthropic, local models), understands your entire repo structure through a repository map, and makes changes that automatically commit with descriptive messages. It's particularly strong for developers who live in the terminal and want full transparency over what the AI changes and why.

Best for: Terminal-oriented developers who want open-source, model-agnostic AI coding with tight git integration and full control over their workflow.

vs Devin: Fully open-source and free. You bring your own LLM. More hands-on but gives you complete control and transparency. No cloud sandbox — everything happens locally.

OpenAI's cloud-based coding agent, available through ChatGPT. Codex spins up a sandboxed environment for each task, reads your repository, writes code, runs tests, and produces a pull request — a workflow similar to Devin's. It connects directly to GitHub repositories and can work on multiple tasks in parallel. The agent runs asynchronously, letting you assign tasks and come back to review completed work.

Best for: Teams already invested in the OpenAI ecosystem who want Devin-style autonomous coding within ChatGPT's interface.

vs Devin: Similar async sandbox model. Bundled into ChatGPT Pro rather than a standalone subscription. More tightly integrated with OpenAI's model ecosystem.

Amazon's AI coding assistant, integrated into VS Code, JetBrains, and the AWS console. Beyond standard code suggestions and chat, Q Developer includes an agent mode for autonomous multi-step tasks — implementing features across files, generating tests, and performing code transformations. Its standout capability is deep AWS integration: it understands CloudFormation, CDK, and AWS service configurations natively, making it the strongest choice for teams building on AWS.

Best for: Teams building on AWS who want AI coding assistance with native understanding of AWS services and infrastructure.

vs Devin: Less general-purpose autonomy but stronger within the AWS ecosystem. Cheaper and more integrated for cloud-native development on Amazon infrastructure.

Sourcegraph's AI coding assistant, built on top of their code intelligence platform. Cody's differentiator is codebase understanding at scale — it uses Sourcegraph's code graph to understand dependencies, call patterns, and relationships across massive repositories. For enterprise teams with large, complex codebases, this context awareness produces more accurate suggestions than tools that only see the files you have open. Available in VS Code and JetBrains.

Best for: Enterprise teams with large codebases who need AI that understands cross-repository dependencies and code relationships at scale.

vs Devin: Focused on context-aware assistance rather than autonomous task completion. Stronger at understanding existing large codebases, less focused on greenfield coding.

An open-source AI coding assistant that runs as a VS Code or JetBrains extension. Continue lets you connect any LLM — OpenAI, Anthropic, local models via Ollama, or your own fine-tuned models — and provides autocomplete, inline editing, and chat within your editor. The open-source approach means full transparency into how the tool works and the ability to customize it for your team's specific needs. It's the most flexible option for teams that want AI coding help without vendor lock-in.

Best for: Teams that want open-source, model-agnostic AI coding assistance with full customization and no vendor lock-in.

vs Devin: Extension-based rather than standalone. No autonomous capabilities — it's an assistant, not an agent. Maximum flexibility and zero lock-in.

One of the earliest AI code completion tools, Tabnine focuses on privacy-first AI coding. It offers code completion, chat, and code generation across all major editors and 80+ programming languages. Tabnine's key differentiator is its approach to IP protection — models are trained on permissively licensed code only, and the enterprise version can run entirely on your infrastructure with no code leaving your network. For regulated industries where code privacy is non-negotiable, this matters.

Best for: Enterprise teams in regulated industries who need AI coding assistance with strong privacy guarantees and IP-safe training data.

vs Devin: Focused on privacy-safe code completion rather than autonomous engineering. Less capable but more compliant for sensitive environments.