Greptile makes your codebase queryable. difflore turns your team's PR review history into rules your AI agent reads on every code-write.
They are adjacent, not direct competitors. This page resists the urge to make them look like a head-to-head when they are really complementary.
Indexes your codebase into a queryable knowledge graph. Ask natural-language questions ("how does authentication work?"), get AI-generated PR reviews that reference other parts of the codebase, or embed Greptile's understanding into your own tools via API.
Extracts rules from your team's past PR review comments and feeds them to your AI coding agent (Cursor / Claude Code / Codex via MCP). Your agent gets back the rules relevant to the file it is currently editing — not a single static blob.
Honest framing
Where difflore wins, we say so. Where Greptile wins, we say so too.
| Feature | difflore | Greptile |
|---|---|---|
| What it does | ||
| Primary use | Inject team rules into AI agent | Codebase Q&A + comprehension |
| Knowledge source | PR review comments + manual rules | The codebase itself |
| Output | Per-file rule cascade | NL answers + PR reviews |
| Surface | IDE / agent (MCP) + terminal TUI | Web app + API + GitHub |
| MCP server | First-class | Not yet (API-first) |
| Codebase Q&A surface | — | ✓ (mature) |
| Works with no review history | Needs ~30+ reviewed PRs | |
| Trust & deployment | ||
| Open-source runtime | ✓ (Apache 2.0 CLI/MCP) | |
| Private deployment | Enterprise | Enterprise |
| BYOK (your own LLM key) | ||
| Code stays on your machine | ✓ (local-first CLI) | ✗ (cloud-indexed) |
| Knowledge & learning | ||
| Per-rule source attribution | ✓ (every rule shows "← learned from <repo>" with source PR + reviewer) | ✗ (their answers cite code, not your team's past judgments) |
| Graph layer | Rule graph (rules superseding/relating to other rules) | Code graph (functions calling functions) |
| Decay-aware ranking | ✓ (category half-life: Correction 365d / Style 30d) | No half-life mechanism advertised |
Not really. Greptile is a codebase-comprehension layer — it makes "what is in your repo" queryable. difflore is a team-policy delivery layer for AI agents — it surfaces "what your team has decided over time" at the moment the agent is writing code. Both are forms of context, but they sit on different shelves.
A codebase has the state but not the decisions. When your senior engineer commented on PR #347 saying "do not use import.meta.dirname , it broke for us in production on Node 18," that decision lives in the PR thread, not in any code file. Git shows the eventual change, not the reasoning. Greptile sees the after-state. difflore captures the why.
They could, but it is a meaningfully different system. Extracting rules from review comments is an LLM-batch-job pipeline, not a search-and-comprehend layer. It would be a separate product line for them — not impossible, just unlikely soon.
difflore's extractor is forgiving. We have tested on small corpuses (cli/cli's 30 reviewed PRs over 6 weeks → 50 usable rules). If you have under 10 reviewed PRs in the last 6 months, the rule library will be thin and you should wait until you have more review history.
Yes — they are complementary. Greptile answers "what does this code do?" for humans and agents that need orientation. difflore answers "what should the agent avoid in this file?" at the moment of code generation. Both can be MCP tools the agent calls in parallel.
Review the current difflore plan details on /pricing, or install the Apache-2.0 local runtime from GitHub.
Greptile feature claims sourced from their public site as of 2026-04-19. Check greptile.com for current product details.