Developer tools decision

Augment Code

A single technical user can build a useful automated PR-reviewer (the common core loop) in ~one week, but reproducing Augment Code's full platform (context engine, shared memory, sandboxes, enterprise controls, and orchestration at scale) is much larger and not practical as a drop-in replacement.

Visit website
Subscription$100/month ✓ verified
Initial build30 hours
Monthly upkeep8 hours + $200
Evidence2/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.

What a replacement has to do

  • Automate first-pass code review on GitHub PRs with an LLM-driven agent that fetches diffs, reasons over context, posts review comments, and optionally opens or updates PRs.

What it still won’t have

  • Shared organization memory and cross-run learning
  • Managed sandboxes and tenant-level isolated execution
  • Built-in enterprise security/compliance (SOC 2, CMEK, ISO) and audit trails
  • Service accounts, per-automation tokens, and usage monitoring
  • Multi-model routing and a catalogue of specialized sub-agents/Experts

What remains hard

  • Execution qualityCosmos runs your software agents at scale, giving them the context, tools, and feedback loops they need to get better with every workflow.
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 3 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal self-hosted 'Agentic PR Reviewer' in Node.js: implement a GitHub webhook receiver (Express), fetch PR diffs and relevant files, produce a summarized code-context prompt, call an LLM via OpenAI-compatible API, parse the model's structured review (inline comments + overall verdict), and post comments/reviews back to GitHub. In scope: webhook handling, context retrieval, prompt construction, model integration, posting GitHub review comments, basic deduplication/state (Postgres), Dockerfile, unit tests for webhook and GitHub client, and CI workflow. Out of scope: multi-tenant orchestration, managed sandboxes, SOC2 compliance, multi-model routing, and production-grade sandboxed code execution. Require error handling, retries, rate-limit backoff, logging, and tests covering happy/error paths.
How we checked4 sources · 2/3 runs agreed · evidence score 63

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 4 cited sources+3
  • Price verified on pricing page+3
  • Evidence score63

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.

How scoring works →

Cited sources · 4

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat quoted from the page