Loading tool details…
AI copilot for JetBrains IDEs — fastest-in-class autocomplete plus a chat agent that can plan, edit, run tests, and browse the web across a project.
AI copilot for JetBrains IDEs — fastest-in-class autocomplete plus a chat agent that can plan, edit, run tests, and browse the web across a project.
Sweep is a JetBrains-native AI coding assistant that positions itself as the fastest autocomplete inside IntelliJ IDEA, PyCharm, GoLand, WebStorm, RubyMine, PhpStorm, CLion, Android Studio, Rider, and the rest of the JetBrains family. It layers three surfaces on top of the IDE: (1) low-latency multi-line 'Tab, Tab, Tab' autocomplete powered by a custom in-house Tab model tuned for the surrounding project context, imports, and language conventions; (2) an in-editor chat with your codebase indexed for retrieval, so you can ask 'where is our retry logic' or 'refactor this to use the async client' and get grounded answers with resolved definitions and code search; and (3) an agent mode that plans a change across files, applies the diff, runs tests, and iterates when they fail. The company originally built an autonomous GitHub-issue-to-PR bot under the same brand; the 2026 relaunch pivots to the in-IDE JetBrains niche, where Cursor and VS Code Copilot have historically been weaker. Sweep uses its own LLMs for pricing, performance, and security reasons, ships as the #1-rated AI plugin on the JetBrains Marketplace (4.9 stars, 40k+ installs), and is SOC 2 compliant with zero data retention. It offers a free download for individuals, a Pro tier for indie developers, and Team / Enterprise plans with SSO, audit trails, and reserved private inference. For teams heavily invested in JetBrains and wary of migrating to VS Code just to get first-class AI, Sweep is one of the more polished options that keeps you in your existing editor.
Sweep automates routine GitHub tasks like bug fixes, small features, and dependency updates through an AI-powered bot that reads issues and creates pull requests. It excels at well-defined, narrow tasks within existing codebases. Not suited for complex architectural work, but valuable for reducing the toil of routine maintenance and small improvements.
Monitors GitHub Issues and automatically implements solutions, creating pull requests with working code and appropriate tests
Use Case:
File a bug report about broken form validation and wake up to a pull request with the fix, tests, and documentation updates
Analyzes project structure, dependencies, and patterns to implement changes that fit seamlessly with existing architecture
Use Case:
Request a new API endpoint and get implementation that follows existing authentication, error handling, and response formatting patterns
Implements features that span multiple files while maintaining consistency and handling all necessary updates including imports and dependencies
Use Case:
Add a new database model and have Sweep automatically update migrations, API routes, frontend components, and related tests
Monitors build results and test failures, making automatic adjustments to ensure pull requests pass all quality checks
Use Case:
Submit a pull request that initially fails tests, then watch Sweep analyze failures and commit fixes until all checks pass
Identifies and resolves code quality issues, deprecated dependencies, and architectural improvements proactively
Use Case:
Have Sweep automatically update deprecated dependencies, fix linting issues, and refactor complex functions for better maintainability
Automatically generates or updates documentation, README files, and test coverage as part of feature implementation
Use Case:
Implement new features and get comprehensive documentation updates and test coverage without manual effort
$0
Per-user monthly
Custom
Ready to get started with Sweep?
View Pricing Options →Sweep works with these platforms and services:
We believe in transparent reviews. Here's what Sweep doesn't handle well:
In 2026, Sweep improved its codebase understanding with repository-wide indexing that enables more accurate multi-file changes. Updates include better test generation alongside code changes, support for monorepo structures, and integration with CI/CD pipelines to automatically verify generated PRs pass existing test suites before requesting review.
Developer Tools
GitHub's agentic dev environment for everyday tasks — take an issue, produce a spec, get a plan, generate the code, and open a PR.
AI software engineer
Devin review for engineering teams: AI software engineer features, pricing, MCP integrations, enterprise controls, pros, cons, and use cases.
Coding Agents
Cursor is the leading AI-first code editor: a VS Code fork with Tab autocomplete, Chat, and — most importantly — Agent mode that reads your whole repo, edits multiple files, runs commands, and finishes multi-step tasks autonomously.
Pricing and features change often. If anything on this Sweep page is wrong or missing, tell us and include a link to the source. We review every correction.
I ran a six-week evaluation of 15 AI developer tools across four workloads: a Flask-to-FastAPI refactor, a Next.js dashboard built from scratch, 23 bug-fix issues sampled from SWE-bench Verified, and a Rust-to-Go port of a 600-line CLI. **Rankings reflect what shipped working cod
Hidden gems in the AI agent tooling space — from browser infrastructure to memory platforms to observability tools. These production-ready tools solve real problems that most developers haven't discovered yet.