Marcos
90cea84f6f
- Dedupe: Gitea-as-state. Scan existing reviews for a hidden
<!-- pragent:sha=... --> marker matching the head sha; skip if present
(kills duplicate reviews on label-toggle / re-fire). Prior review bodies
fed back as 'already said' context (light framework §6.1).
- Repo-local focus: optional .pr-review.json at repo root
({focus,exclude_paths,languages,instructions}), fetched at head ref.
- Inline comments + apply-able suggestions: model emits JSON findings
{severity,path,line,problem,fix,suggestion}; diff hunks parsed into valid
(path,new_line) RIGHT-side anchors; anchored findings become positional
review comments with a ```suggestion fence (Gitea apply-button);
unanchored findings fold into the summary body.
- Tests: parse_diff_anchors, parse_findings (tolerant JSON), split_findings,
inline_comment_body, summary_bullets, parse_repo_config, reviewed_shas,
prior_review_bodies, sha-marker. 35 pass.
- Bump OLLAMA_MAX_TOKENS default 6000 -> 8000 (suggestions add length).
Co-Authored-By: Claude <noreply@anthropic.com>
pragent
An extensible, forge-agnostic PR review framework. Not a product — a toolkit that teams extend with their own review dimensions.
Status: design approved; framework build deferred. A pilot is live on
glm-5.2:cloud with two delivery paths:
- Central webhook service (preferred, least per-repo setup): a Gitea
user-level webhook posts PR events to an always-on in-cluster service that
gates on the
AI-REVIEWlabel. Onboarding a repo = addpragent-botcollaborator + create the label + label a PR. Seepilot/README-webhook.md. - CI-step (legacy): a per-repo Gitea Action fetches the reviewer script at
runtime. See
pilot/README.md.
The framework design remains at
docs/plans/2026-08-04-pragent-design.md;
the pilot is its bootstrap and will be superseded by pragent review when the
framework build resumes.
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 | ~$0.80–2.00 |
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.
- Analyzers — drop a YAML + prompt in
.pragent/analyzers/, or install from npm - Forge adapters — Gitea, GitLab, GitHub, local diff
- Tier policy — thresholds and the path risk map, per repo or per org
- Profile enrichers — extend what
pragent initlearns about a repo - 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
- Walking skeleton — local diff, one analyzer, rules-only tiering
- Gitea end to end — adapter, Woodpecker step, PR comments, status checks
- Profile + full tier —
pragent init, shared-prefix caching, analyzer fan-out - Extensibility hardening — plugin loading, config layering,
explain/replay - Second forge — GitLab adapter, Jenkins recipe
- Analytics maturity — OTel export, feedback loop, eval harness
License
TBD.