AI Features

PR Flow doesn't sell you an AI model — it orchestrates the AI you already trust. Prompts, code, and results never touch PR Flow's servers; inference runs wherever your provider runs. You pick the provider (or a different one per task), and every result is labelled with exactly what produced it.

All of PR Flow's AI lives behind a single switch. Deep review, complexity and risk, thread summaries, and the rest are built-in agents you turn on — together — by enabling Agent Pipelines, then switch on or off individually and tailor to your team's voice. This page covers choosing a provider and what the built-in helpers do; to build your own agents, see Agent Pipelines.

Choosing your AI provider

Configure everything under Settings → Integrations → AI providers. You can use any of:

• Built-in CLIs — the Claude, Antigravity, or Codex command-line tools, authenticated with your own account. These can fetch extra repo context during a review.
• Ollama or any OpenAI-compatible endpoint — point PR Flow at a local model server (Ollama, LM Studio, llama.cpp, vLLM, LocalAI) or a hosted/company gateway. Prompts to a localhost endpoint never leave your machine.
• Any local CLI — bring your own command-line AI tool (aider, llm, opencode, ...) by giving PR Flow its command and how to pass the prompt.

Each provider row shows a plain-English data-flow label (where its prompts go), a Test button, and — for hosted endpoints — an API-key field that's stored encrypted and never written to your settings file.

For a team-managed HTTP gateway, the administrator supplies the approved endpoint and model while each seat enters its own API key locally. The key receives the same encrypted local storage as a personal provider and is never placed in the team's license policy. Custom CLI providers are also checked before saving: argument- and file-based delivery must include the required prompt destination, so a run can't silently invoke a tool without the review prompt.

Route each task to the right provider

The Task routing table lets you choose which provider handles each job — deep review, complexity & risk, thread summaries, discuss, and review-summary drafts. A common setup is a fast local model for frequent summaries and a top-tier CLI for deep review. Anything left on "App default" uses your default provider, so one provider outage only degrades one task class.

Prerequisites: install and authenticate a CLI, run a local model server, or have an endpoint URL ready — then add it under Settings → Integrations. Once licensed, AI features run automatically.

Deep Review & Insights

When you open a PR in the detail slide-over, PR Flow shows its AI insights. The complexity and risk read computes automatically as PRs land on your board; a full deep review is a click away — or automatic too, if you switch its auto-run on.

• Risk & Complexity: Every PR receives an estimated complexity label (e.g., easy to extreme) and a risk level (safe to critical). When new commits change the PR head, the old estimate is marked outdated in the feed, detail, and review views until you choose Re-evaluate.
• Effort Estimate: An AI-generated estimate of how many minutes it will take a human to review the PR.
• Thread Summaries: Your chosen provider distills long, chaotic review threads into a clean summary of what was asked, what was clarified, and what is still open.
• Inline Findings: The AI generates specific findings pinned directly to the exact lines of code in the unified diff. You can accept a finding to turn it into a staged comment, or dismiss it.

Every result carries a small caption naming the provider and model that produced it — and flags when a run had to skip repo-fetch tools (some providers can't run them), so you always know what you're looking at.

Manual deep reviews follow your Reactive pings notification preference. Automatic deep reviews finish quietly, so enabling auto-run doesn't produce background completion noise.

Discuss with AI

Not sure why a specific change is risky? PR Flow features a side-panel discussion interface right next to the code.

You can ask the AI about:

• A specific file
• A specific AI finding
• The entire PR

You can then take any of the AI's answers and drop them straight into your review as a comment.

The destination follows the scope you chose: a whole-PR answer joins the review summary, while a file-scoped answer becomes a staged file comment. Finding-scoped questions include the exact finding context whether it came from the built-in deep review or one of your own agents.

Audit logs

Two separate, on-device logs keep everything transparent — both stay on your machine and are never synced:

• Settings → Logs → AI runs — a record of every AI run: the feature, provider, model, size, duration, and outcome, with JSON export. It's metadata-only by default; storing prompt contents is an explicit opt-in.
• Settings → Logs → Write actions — because PR Flow acts as an assistant, nothing is posted to GitHub/GitLab without your explicit submit. This log captures every comment, reply, review, and nudge it wrote on your behalf.

For teams

Organizations can standardize AI on an approved provider or internal gateway. A team's license can lock AI to specific providers, enforced on every seat — so "code only goes to our own gateway" is a guarantee, not a per-person setting. A locked policy with no providers intentionally disables AI on every seat instead of falling back to a personal provider. Reach out for Enterprise setup.