# 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-REVIEW` label. Onboarding a repo = add `pragent-bot`
  collaborator + create the label + label a PR. See
  [`pilot/README-webhook.md`](pilot/README-webhook.md).
- **CI-step** (legacy): a per-repo Gitea Action fetches the reviewer script at
  runtime. See [`pilot/README.md`](pilot/README.md).

The framework design remains at
[`docs/plans/2026-08-04-pragent-design.md`](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the pilot is its bootstrap and will be superseded by `pragent review` when the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.

## What it costs

The pilot runs on `glm-5.2:cloud` through the on-network headroom proxy, so today it
bills nothing per token — but the token *work* is real, and `pilot/cost_model.py`
prices it against published API rates. Factory prompt sizes are measured from the
files in this repo; the per-tier workloads are calibrated against runs actually
measured through the `AI-USAGE` label (`OBSERVED_RUNS` in that file).

**The measured anchor.** The hardening PR (`#7`, 16 files / ~1100 changed lines,
tier `full`) took 28 agent steps and 348s, and consumed **2,071,025 input** and
**17,303 output** tokens — with **zero cache reads or writes**, because the current
headroom/glm path does no prompt caching. Priced elsewhere, that single review is:

| Model | that review | blended per PR | 350 PRs/month |
|---|---:|---:|---:|
| Claude Opus 5 | $10.79 | ~$4.97 | ~$1,740 |
| GPT-5.6 Sol | $10.87 | ~$5.02 | ~$1,755 |
| Claude Sonnet 5 | $4.32 | ~$1.99 | ~$696 |
| GPT-5.6 Terra | $4.35 | ~$2.01 | ~$702 |
| Claude Haiku 4.5 | $2.16 | ~$0.99 | ~$348 |
| GPT-5.6 Luna | $0.43 | ~$0.20 | ~$70 |

Blended figures use a 5/35/55/5 tier mix with caching off, matching what is
actually observed. Run `python3 pilot/cost_model.py --help` for other mixes and
volumes.

Two things dominate, and neither is the diff:

1. **The loop resends its context every step.** 28 steps over a ~17k-token diff
   produced 2M input tokens. Cost is roughly quadratic in step count, which is why
   `attention-tiering` caps steps, file reads and subagent fan-out per tier.
2. **Prompt caching is worth about a third of the bill** and is currently not
   happening. Any move to a paid provider should confirm the `cache_read` column
   goes nonzero before budgeting.

An earlier version of this model assumed 12 steps and caching on, and was ~15x
low. The lesson is in the file: budget from `OBSERVED_RUNS`, not from the tier
table, and append a row every time a real review reports usage.

## 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.
- **The reviewed code is untrusted input.** The reviewer runs an agent over a
  branch anyone with PR access can write. So it holds no credentials in its
  environment, the checkout is stripped of files an agent runtime would load as
  instructions, PR-authored text is fenced as data, and reviewer config is read
  from the base branch. See "Threat model" in
  [`pilot/README-webhook.md`](pilot/README-webhook.md).

## Stack

TypeScript + Node, built on the [`pi`](https://github.com/badlogic/pi-mono) 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.