Continue.dev Review 2026: Free AI Coding, CI Agents, and When to Pick It Over Cursor

#continuedev #review #opensource #vscode

This article was originally published on aicoderscope.com

Ask any developer who switched from Continue.dev to Cursor in 2024 whether to bother revisiting it in 2026, and you'll get an answer that's about 18 months out of date. The tool they remember — a free, bring-your-own-API-key VS Code extension that competed squarely on the "open-source Cursor" angle — still exists and still works. But it's no longer what the company is primarily building, and that changes the calculus for who should use it.

Since mid-2025, Continue has split into two distinct products that share a repo, a website, and a pricing page but address completely different problems. One is for writing code in an IDE. The other is for reviewing code automatically on every PR. Getting clear on which half matters for your workflow is the entire point of this review.

What Continue.dev Actually Is in 2026

The IDE extension remains free, open-source (Apache 2.0 license), and installable in both VS Code and JetBrains IDEs. It crossed 2.5 million VS Code installs and 33,000 GitHub stars as of May 2026. Autocomplete, chat, multi-file edit, and agent mode are all included — powered by whichever model or models you configure. Nothing has been shut down here; the team just stopped treating the IDE extension as the company's main product.

The CLI — the cn command — runs async AI agents on pull requests as GitHub status checks. Each agent is a markdown file stored in .continue/checks/ inside your repo. Push a PR, the agents run against the diff, and you get either a green checkmark (code passes) or a red status with a suggested patch (code fails, here's how to fix it). The company calls this "Continuous AI," and it's where their roadmap energy is visibly pointed.

Both products share the same model layer: bring an Anthropic, OpenAI, or Google key, point at a local Ollama instance, or use any of 100+ supported providers, and Continue routes requests to it. But the use cases they solve are different enough that treating them as the same product causes confusion.

The IDE Extension: Where It Wins and Where It Trails

For everyday coding, the IDE extension earns its place for a specific developer profile: someone who wants per-feature model control, uses JetBrains IDEs, or needs inference to stay on their machine.

Autocomplete is the strongest feature. Configure a fast local model — Qwen2.5-Coder 7B via Ollama works well — for inline tab completion, then use a heavier cloud model for chat and agent tasks. This split-model setup is something Continue makes trivially configurable via a few lines in config.yaml. Cursor Pro charges $20/month and does not let you tune which model fires at which interaction tier. GitHub Copilot lets you pick your chat model but routes autocomplete through its own pipeline. Continue gives you complete control.

The practical result: for pure autocomplete speed and API cost, Continue wins. When you're using Claude Sonnet 4.6 for completions through Continue, you pay Anthropic's input/output token rates directly — no markup, no usage cap, no overage. Cursor Pro includes a monthly quota on Claude calls; once you hit it, you're paying more. Continue Solo never has this problem because you're billed directly by the provider.

Chat and agent mode are a different story. They work, and for routine tasks — explain this function, refactor this method, generate a unit test for this class — they perform well. Multi-file edits land correctly more often than not. But agent mode lags behind Cursor Composer and Cline on longer or more ambiguous tasks. Users who've run direct comparisons consistently flag the same gap: Continue's agent mode offers less visibility into what the agent is doing at each step, and when a task derails, diagnosing why is harder. Cursor's Composer thread makes the reasoning chain explicit; Continue's agent mode is more of a black box.

This gap matters most for complex agentic work: adding a new API endpoint across three files, migrating a 500-line module to a new pattern, scaffolding a feature from a spec. For tasks like "add a docstring to this function" or "write a test for this class," the gap is irrelevant.

JetBrains support is Continue's most underappreciated advantage. Cursor is a VS Code fork — it does not run in IntelliJ, PyCharm, or GoLand. Cline is VS Code-only. GitHub Copilot supports JetBrains, but it costs $10/month per seat. If your team standardizes on IntelliJ for Java or Kotlin work and wants a free, privacy-respecting AI layer that supports local models, Continue is the answer. Nothing else in the open-source space fills that exact gap.

For the full configuration walkthrough — config.yaml structure, context providers, multi-provider setup, and per-language rules — the Continue.dev configuration guide covers it in depth. The current review focuses on product decisions, not setup steps.

The CLI: AI Quality Checks That Live in Your Repo

This is the product that genuinely has no direct competitor in the current AI coding tool landscape, and the reason Continue belongs on a watchlist even if you're happy with Cursor for daily development.

The concept is simple to state but under-used in practice: you write a markdown file describing a code review rule, store it in .continue/checks/ in your repo, and it becomes a GitHub status check that runs on every PR. The check either passes (green) or fails with a suggested diff showing exactly how to fix the issue.

The cn CLI provides three key commands:

cn checks [pr-url] — runs all checks against a specific PR
cn checks --diff — runs checks against the current diff locally before pushing
cn checks accept — applies the suggested fixes from a failed check

Checks run asynchronously, meaning multiple agents execute in parallel. Continue integrates directly with GitHub, and also with Sentry for error context, Snyk for security scanning, and standard CI/CD pipelines. The agents are defined in plain markdown — no proprietary DSL, no GUI configuration panel — which means they live in version control like any other file, get reviewed in PRs, and can be rolled back if they cause false positives.

A team that defines five checks — "no hardcoded secrets," "all public functions need docstrings," "no TODO comments older than 14 days," "test coverage must not drop," "API responses must have error handling" — can enforce those standards automatically before any human reviewer sees the PR. The async architecture means these run in the background without blocking the PR submission.

The friction point: setup requires someone comfortable with CI/CD configuration and writing effective agent prompts. "Write a check that flags hardcoded credentials" is obvious; "write a check that ensures every new database query uses the team's connection pool pattern" takes real prompt engineering. The quality of your checks directly reflects the quality of the people who wrote them, which is also true of any code review checklist — but here you're also responsible for the prompt that powers it.

For teams with messy CI/CD pipelines: sort the pipeline first. Adding AI checks on top of a brittle CI setup creates more noise than signal.

Pricing: Free Tier, Team Plan, and the Models Add-On

Plan	Price	What You Get
Solo	Free	Full IDE extension, full CLI, BYOK, local models
Team	$10/dev/mo	Centralized config, shared agents, secure secret management, SSO
Enterprise	Custom	Advanced governance, priority support, self-hosting
Models Add-On	Contact sales	Frontier model access without managing individual API keys