Claude Code handles large repositories best, because agentic search finds relevant code more reliably than index-based retrieval as a codebase grows. Codex CLI and Amp are strong alternatives, Cline is the best option inside VS Code, and Aider remains the most controllable for surgical, git-native changes.
Editorial judgement last reviewedEvery AI coding tool works on a small project. The differences only appear at scale, and they appear sharply: past a few hundred thousand lines, the binding constraint stops being how well the model writes code and becomes how well the tool finds the code that matters.
Two strategies compete. Index-and-retrieve builds an embedding index of the repository and fetches semantically similar chunks, fast, cheap, and prone to returning the plausible file rather than the correct one. Search-and-explore greps, reads and follows imports the way a new engineer would, slower, more expensive per task, and markedly better at questions whose answer is spread across code you weren't already looking at.
Our coding agents comparison covers eighteen harnesses on context strategy, autonomy, model support, sandboxing and pricing.
We ranked specifically for large repositories, which is a different question from "which is best overall".
Context strategy. How the agent decides what to read. Agentic search degrades far more gracefully with repository size than retrieval does.
Multi-file editing. Whether a coherent change across twenty files stays coherent, and whether the agent verifies its own work by running tests.
Iteration. Whether it runs the test suite, reads the failure and keeps going, or hands you a diff and stops.
Project memory. Support for a checked-in instructions file, conventions, commands, architecture, which is the single highest-leverage configuration you can do for any of these tools.
Cost per completed task, not per hour. An agent that costs three times as much and finishes the job is cheaper than one that doesn't.
Explores rather than retrieves. It greps, reads files, follows imports and builds its own picture of the codebase on demand, which is why it holds up when a repository is too large for any index to be reliably useful. "Why does this only fail in CI" is the shape of question it answers well, because the answer is usually scattered.
It also closes the loop: makes the edits, runs the tests, reads the failures, fixes them, repeats. A single instruction can produce a dozen file edits and three test runs, which is a lot of leverage and a lot of diff to review.
Project instructions and reusable skills check into the repository, so the agent's behaviour is version-controlled alongside the code and every developer's agent behaves the same way. Non-interactive modes let the same agent run in CI or a git hook.
Token-hungry on heavy work. Measured per completed task rather than per hour, it's competitive.
OpenAI
's terminal agent, with the same broad shape: explore the repository, edit files, run commands, iterate. Sandboxing is a first-class concern, with configurable approval modes that let you decide how much rope the agent gets before it needs to ask.
Open source, which matters if you want to understand or modify the harness rather than treat it as a black box. Model quality tracks OpenAI's releases, and if your organisation is standardised on OpenAI for procurement or data-handling reasons, this is the natural pick.
Slightly less mature tooling around project-level configuration than the leader, and the ecosystem of shared conventions is smaller.
Built by the Sourcegraph team, whose whole history is code search at scale, and it shows in how it navigates large repositories. Amp leans into parallel subagents: fan several out across different parts of a codebase, then reconcile. On a genuinely large monorepo that parallelism is a real speedup rather than a demo.
The model routing is deliberately opinionated, choosing the right model for each step rather than asking you to. That's less control and less to think about, which suits teams who want the tool to have opinions.
Worth trialling specifically if your problem is a monorepo where a single agent spends most of its time navigating.
An open-source agent that runs as a VS Code extension, so you keep your editor, your extensions and your review surface while getting genuine agentic behaviour, plan then act, multi-file edits, terminal commands, browser interaction.
Bring your own API key and route to whichever model you want, which makes cost entirely visible and controllable. That transparency is the main reason people choose it: you see exactly what each task costs.
Context handling is good but bounded by the editor's view of the world, so it doesn't scale to enormous repositories as gracefully as a terminal agent that searches freely. For most codebases that's an acceptable trade for staying in the editor.
The original terminal coding agent and still the most surgical. Aider
is git-native, every change is a commit with a sensible message, so reverting is trivial and the history stays readable. You add files to the context explicitly, which means you always know exactly what the model can see.
That explicitness is both the strength and the limit. On a large repository, choosing the files yourself is work the newer agents do for you. But when you know precisely which five files need changing, Aider is the fastest and cheapest path, with no exploration tokens spent.
Works with essentially any model, including local ones, which makes it the pragmatic choice when code cannot leave your infrastructure.
The highest-leverage twenty minutes you can spend on any of these tools is writing a project instructions file, the conventions, the build and test commands, the architectural constraints, the things a new engineer would need told.
Without it, an agent rediscovers your project from scratch on every task. It runs the wrong test command, guesses at your formatting, puts files in the wrong directory, and burns tokens reading code purely to infer conventions you could have stated in three lines. With it, the same agent gets to the actual work immediately.
Keep it specific and short. "Run tests with bun test." "This package must not import from that one." "We use tabs." "Migrations live here and are never edited after merge." Long documents get partially ignored; precise constraints get followed.
This is also the artefact that transfers. If you switch tools, and you will, the instructions file moves with you, because every serious harness reads a project-level convention file. It's the closest thing to a portable investment in this category, and it makes every developer's agent better simultaneously.
An agent that edits twenty files in one go is a productivity multiplier and a review problem. The characteristic failure isn't code that doesn't compile, that gets caught immediately. It's a confidently completed task built on a wrong assumption made in step two, where the tests pass because the agent also adjusted the test.
Four habits handle almost all of it. Commit before you start, so the agent's diff is clean and revertible. Scope tasks to something you can actually read, a feature, not a rewrite. Read the diff like it came from a contractor you haven't worked with before, because it did. And be suspicious of any change to a test file that accompanies a change to the code it tests.
On large repositories there's one more: check the blast radius. Agents are good at finding call sites and bad at knowing which ones are load-bearing. A rename that touches forty files across three packages deserves more scrutiny than the size of the diff suggests, not less.
The teams getting the most out of these tools are not the ones reviewing least. They're the ones who scope tightly, so review stays cheap and they can run more tasks.
Yes, and it's where the differences are sharpest. Agentic search handles a monorepo better than index-based retrieval. Scope each task to a package where you can, and use an instructions file to explain the layout, it saves an enormous amount of exploration.
More than on a small one, because exploration means reading files. Judge cost per completed task rather than per hour. Most of the waste comes from an agent rediscovering conventions you could have written down.
Standardise on the instructions file, not the tool. That's what makes every agent better and it transfers when someone switches. Let developers pick the harness; make the repository's conventions explicit.
