Marcos 9b017475c9 docs: add phase 1 implementation plan
TDD task breakdown for the walking skeleton: diff parsing, forge adapter port
with a local git implementation, rules-based tiering, config loading, analyzer
runner behind a ModelClient port, pi SDK client, aggregator, JSONL emitter, CLI.
Later phases recorded as milestones with done-when criteria.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011Ye1KNFMkkUtmzTypHXkoK
2026-08-04 16:40:05 +00:00

pragent

An extensible, forge-agnostic PR review framework. Not a product — a toolkit that teams extend with their own review dimensions.

Status: design approved, implementation not started. See docs/plans/2026-08-04-pragent-design.md.

What it is

pragent runs as a CI step. It reads a pull request, decides how much attention the change deserves, runs the analyzers that apply, and posts ranked findings back to the forge.

pragent init      # one-time repo scan → .pragent/profile.yml (committed, reviewable)
pragent review    # the CI step: tier → analyze → aggregate → publish
pragent explain   # why did this PR get this tier / these findings?
pragent replay    # re-run a past PR against a new prompt or model (the eval loop)
pragent doctor    # config, credentials, and adapter health

Why not CodeRabbit / Greptile / Qodo

Those are good products with fixed review dimensions and per-seat pricing. pragent targets the case where a platform team needs to add its own dimensions — an internal compliance rule, a service-catalog ownership check, a house performance idiom — without forking a vendor's reviewer. Cost lands in the same range (~$25/dev/month at 350 PRs/mo for 20 devs), but the analyzers, the data, and the analytics are yours.

Attention tiers

Every PR is classified before any expensive work happens. Deterministic rules decide first; an ambiguous case gets one cheap model call as tie-breaker.

Tier What it means Cost/PR
trivial lockfile bumps, generated code, docs typos ~$0.005
lite small change, no risk paths ~$0.08
full the default for real changes ~$0.802.00
oversized too big to review whole; structural summary + deep pass on the hot subset ~$5 ceiling

Every tier decision records why, so a surprising outcome is explainable rather than mysterious.

Extension points

Five, all documented in the design doc. Teams override or add; nobody forks.

  1. Analyzers — drop a YAML + prompt in .pragent/analyzers/, or install from npm
  2. Forge adapters — Gitea, GitLab, GitHub, local diff
  3. Tier policy — thresholds and the path risk map, per repo or per org
  4. Profile enrichers — extend what pragent init learns about a repo
  5. Emitter sinks — JSONL by default, OpenTelemetry, or your own

Org config can lock keys, so a repo cannot quietly disable the security analyzer.

Design principles

  • Polyglot by construction. Language knowledge lives in the repo profile, not in the reviewer. A new language is a profile change, not a core change.
  • One shared prompt prefix. All analyzers for a PR share a byte-identical cached prefix. This is what makes fan-out affordable; it is enforced, not hoped for.
  • Everything is traceable. Tier reasons, token counts, cost, latency, and finding outcomes are recorded per run. False-positive rate is measurable per analyzer.
  • Fail open. A budget ceiling or an analyzer crash yields a partial review with a clear note, never a blocked pipeline with no explanation.

Stack

TypeScript + Node, built on the pi agent SDK. Shipped as an npm package and an OCI image, so CI runners need no local Node install.

Roadmap

  1. Walking skeleton — local diff, one analyzer, rules-only tiering
  2. Gitea end to end — adapter, Woodpecker step, PR comments, status checks
  3. Profile + full tier — pragent init, shared-prefix caching, analyzer fan-out
  4. Extensibility hardening — plugin loading, config layering, explain / replay
  5. Second forge — GitLab adapter, Jenkins recipe
  6. Analytics maturity — OTel export, feedback loop, eval harness

License

TBD.

S
Description
An AI pull-request reviewer for Gitea — inline findings with suggested fixes, whole-repo context, and per-review cost reporting.
Readme 2.1 MiB
Languages
Python 99.5%
Dockerfile 0.5%