# Build brief — a focused alternative to Greptile > **Verdict:** Partly, if you narrow it · **Buildability:** 46/100 · **Category:** Dev Tools > **Source:** https://www.canitbevibecoded.com/greptile > Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Greptile. Verify current pricing and capabilities before acting. ## Context **Greptile** — An AI bot that indexes your whole codebase and leaves context-aware review comments on every pull request. It currently costs $30/mo. The mechanical core is genuinely a focused project: a GitHub App that catches pull request webhooks, pulls the diff, retrieves related code from an embedding index of the repo, and posts inline comments from an LLM. You can get to first useful comment with a focused implementation. What you will not get in a focused implementation is signal quality, which is the entire product: knowing when to shut up, not re-flagging the same nit on every push, understanding a monorepo without blowing the context window, and keeping the index fresh without a full reindex on every merge. Expect a bot that is impressive on day one and muted by the team on day nine. Worth building if you own the repo and enjoy tuning prompts; not worth building to save a per-seat fee across a real engineering org. This brief describes a focused, single-operator replacement for the part of Greptile that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest. ## What you are building A GitHub App that on each pull request retrieves semantically related code from a local embedding index of the repo and posts LLM-written inline review comments on the diff. - Build the focused developer workflow you use repeatedly, with local configuration. - A responsive interface with real empty, loading, success, and error states. ## Requirements ### Functional - Local disk for the vector index, roughly proportional to repo size. - Willingness to iterate on the review prompt for weeks. ### Data and integrations - GitHub App registration with webhook secret and private key. - An LLM API key (embeddings plus a strong reasoning model). - A small always-on host or tunnel to receive webhooks. Each of these needs a real account, credential, or quota. Set them up before writing feature code. ### Non-functional - Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support. - Security: server-side secrets, validated input, and no credentials in the client bundle. - Reliability: retries with backoff on external calls, and a clear failure state when a provider is down. - Portability: the operator can export their data and leave without losing it. ## Implementation brief Build a self-hosted AI pull request reviewer for a single GitHub repository. No web UI, no accounts, no telemetry. Stack, no substitutions: Python 3.12, FastAPI, uvicorn, SQLite for state, sqlite-vec for vector search, httpx for GitHub REST calls, OpenAI API for embeddings and review generation. Package with uv. Everything runs in one process plus one CLI. Secrets in .env, loaded with python-dotenv: GITHUB_APP_ID, GITHUB_PRIVATE_KEY_PATH, GITHUB_WEBHOOK_SECRET, OPENAI_API_KEY, REPO_FULL_NAME. Part 1, indexer CLI (index.py): - Walk the local clone of the repo, respect .gitignore, skip binaries, lockfiles, and anything over 400 KB. - Chunk source files by function or class using tree-sitter for Python, TypeScript, JavaScript, and Go; fall back to 60-line sliding windows with 10-line overlap for everything else. - Embed each chunk, store text, path, start line, end line, git blob sha, and vector in SQLite. - Support incremental reindex: given two git revisions, only re-embed chunks whose file blob sha changed. Print counts of added, updated, deleted chunks. Part 2, webhook service (server.py): - POST /webhook, verify the X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET, reject on mismatch. - Handle pull_request opened and synchronize events only. Enqueue work in a SQLite-backed job table and return 202 immediately. - A background worker fetches the PR diff, splits it per file hunk, and for each hunk retrieves the top 8 related chunks by vector similarity plus the full current version of the changed file if it is under 800 lines. - Send one LLM call per changed file with the hunk, retrieved context, and a review prompt that demands: only comment on correctness, security, or clear API misuse; no style nits; no praise; no summaries; return an empty array when the change is fine. - Model output must be strict JSON: a list of objects with path, line, severity, body. Validate with Pydantic and drop anything whose line is not inside the diff. Part 3, dedupe and posting: - Store a hash of path plus normalized comment body per PR. Never post the same finding twice across force pushes. - Post surviving comments as a single GitHub review with inline comments, authenticated as the GitHub App via a short-lived installation token. - Add a CLI command 'review-local PR_NUMBER' that prints what it would post without calling GitHub, for prompt tuning. Out of scope: GitLab and Bitbucket, multi-repo support, a dashboard, learning from dismissed comments, autofix suggestions, chat replies to review threads. Deliver a README with GitHub App setup steps, the exact permissions needed (contents read, pull requests write, metadata read), and how to expose the webhook locally with a tunnel. Include pytest coverage for signature verification, diff line mapping, and dedupe. ## Delivery standard - Inspect the repository first, then write a short implementation plan before writing code. - Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data. - Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations. - Include responsive layouts plus genuine empty, loading, success, validation, and failure states. - Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data. - Add structured logs around every external call and return actionable errors without leaking sensitive details. - Write unit tests for the core logic and one automated test of the main user journey. - Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations. ## Acceptance criteria - [ ] A clean install starts the app using only the README and .env.example. - [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable. - [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state. - [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control. - [ ] Tests, type checking, linting, and a production build all pass with no ignored failures. - [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified. ## Non-goals Do not build these, and do not claim to have replaced them: - Tuned false-positive suppression: their bot has been beaten into silence by thousands of teams, yours has not. - Incremental reindexing and monorepo handling that does not choke on a 500k file tree. - Memory of past reviews so the same nit is not raised on every force push. - Team-level config, custom rule sets, and per-repo style learning from accepted or dismissed comments. - Bitbucket, GitLab, and self-hosted host support, plus SOC 2 paperwork your security team will ask for. ## What you still own after launch - Secure credentials, rotate secrets, and handle provider rate limits. - Run migrations, backups, restores, and dependency updates. - Test the critical journey after every model, API, or hosting change. - Monitor failures and fix the edge cases a first prompt will miss. - Maintain every third-party integration as APIs and OAuth rules change. ## Risk **Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost. Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet. --- Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/greptile