Reigner

A harness for question-answering agents over your own documents — bounded context, bounded cost, every claim cited.

Point an agent at a pile of documents and it degrades the same way every time: context fills up, cost climbs, and by the twentieth turn you can't tell which answers are grounded in a source. The answering was never the hard part. The machinery around it was.

Reigner is that machinery — a harness for citation-faithful question-answering agents over a knowledge corpus. A small, legible agent loop with explicit guardrails for context and spend, plus the ingestion, tools, sessions and evals to aim it at your own documents. First you ingest your sources — a one-time compile step that reads your raw documents (PDF, text, HTML) and extracts the facts you care about into a structured store, its artifacts. A single retrieval agent then answers questions over that store — never touching your raw files at query time — with every factual claim traced back to its source.

It's a library, not a product: not a chat app, not a multi-agent orchestrator, and a harness for retrieval agents rather than coding agents — Claude Code and Codex CLI have that covered. Reach for Reigner when you want a Q&A agent shaped to your corpus and use case: the instructions, the tools, the schema, the model are all yours to change. Within that flexibility two things stay hard — citation-faithfulness is a testable property (the eval fails uncited claims), and a cheap model handles the routine work so cost stays bounded.

One core, three surfaces

Reigner is a library: you write one agent and reach it three ways as it grows from an idea to a running service. The agent is a small project folder — a REIGNER.md of instructions, a schema, and your tools — and it stays the same folder at every step.

• Build — Write your agent as code. reigner init scaffolds the project folder for you: the REIGNER.md instructions, a schema and extractor (how your documents become searchable), plus any @tools or plugins you add. This folder is your agent.
• Test — Run it from your terminal. ingest compiles your documents, chat asks questions, and session fork / replay / export / eval let you A/B/C different instructions, tools, or models — so you tune without starting over.
• Ship — Serve it over HTTP. serve exposes that same folder through a FastAPI + SSE endpoint, so your apps consume it with no rewrite.

MCP export is planned

reigner serve --http works today. reigner serve --mcp is scaffolded but not yet wired — it exits with a clear "not yet implemented" message. The "ship" surface is FastAPI/SSE for now.

Where to go next

• Usage guide — hands-on, install → scaffold → ingest → chat, with a per-feature status flag on everything.
• Architecture — the harness: the agent loop, oracle escalation, and the G1–G11 context-management guardrails.
• Observability — turn the agent loop into OpenTelemetry spans.
• Principles — why Reigner exists and the rationale behind each design decision.
• API reference — the typed public API.

Install

uv add 'reigner[anthropic,ingestion]'      # model adapter + PDF/URL loaders

Reigner ships a thin core; each capability is an opt-in extra — see the usage guide for the full extras table.