reigner 0.9.0

A single agent that answers from your compiled corpus — every claim cited.

A single agent that answers from your compiled corpus — every claim cited.

📚 Documentation: https://construct-lab.github.io/reigner/

Reigner is a toolkit for building citation-faithful question-answering agents over a knowledge corpus. You compile your sources into bounded, schema-aware artifacts once, then a single retrieval agent answers over them — every factual claim traced back to its source. It is a library first: not a chat app, not a coding-agent harness, not a multi-agent orchestrator.

One core, three surfaces

You meet the same agent core — the harness, the artifact store, and a single REIGNER.md instruction file — at three points in its lifecycle:

• Build — define a per-project agent as a library: a schema, @tools, an extractor, a recipe, plugins. This is what you ship.
• Test — iterate from the CLI: ingest, chat, then session fork / replay and eval to A/B/C variants of your REIGNER.md, tools, or model.
• Ship — serve the same agent over HTTP (FastAPI + SSE) so your apps consume it with no rewrite. (MCP export is planned; see status below.)

Features

• Compiled artifacts — ingestion compiles raw documents into a bounded, schema-aware store. The agent queries the compiled graph, never your raw files.
• Bounded, self-describing tools — every tool result reports has_more, truncated, and available_keys, so a finite-context model always knows whether it got everything.
• Citations are first-class — numeric and factual claims register a CitationEvent with provenance; the eval suite fails answers that make uncited claims.
• Forkable sessions — durable JSONL sessions on disk that you can fork and replay to compare variants without re-running from scratch.
• One typed event protocol — CLI, HTTP, and MCP all consume the same typed events.
• Provider-agnostic — Anthropic, OpenAI, and Gemini adapters behind one interface.

Quickstart

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

reigner init mydocs --recipe document_qa   # scaffold a project
# drop your PDFs/text into mydocs/, then:
cd mydocs
reigner ingest                             # compile documents into artifacts
reigner chat                               # ask questions, get cited answers

The full walkthrough — every command with real output and a per-feature status flag — is in the usage guide.

Install

uv add reigner

Reigner ships a thin core; each capability is an opt-in extra:

Extra	Purpose
reigner[anthropic]	Anthropic model adapter
reigner[openai]	OpenAI model adapter
reigner[gemini]	Gemini model adapter
reigner[server]	FastAPI HTTP server with SSE
reigner[mcp]	MCP server export (planned — not wired yet)
reigner[ingestion]	PDF/URL loaders for the ingestion pipeline
reigner[otel]	OpenTelemetry metrics plugin
reigner[all]	Everything above

License note

Reigner itself is MIT-licensed. The [ingestion] extra pulls in PyMuPDF, which is AGPL-3.0. Downstream projects that distribute or network-serve a closed-source product on top of reigner[ingestion] must comply with AGPL or obtain a PyMuPDF Pro commercial license. To avoid the AGPL entirely, override LLMExtractor.raw_to_text with a permissive-licensed loader of your choice.

Learn more

• Usage guide — hands-on, install → scaffold → ingest → chat.
• Observability — OpenTelemetry spans for the agent loop.
• Principles — the rationale behind each design decision.
• API reference — the typed public API.

Development

uv sync --all-extras --group dev
uv run pre-commit install
uv run pytest

CI runs ruff check, ruff format --check, mypy, and pytest on every PR.

License

MIT — see LICENSE.