reigner 0.10.0

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. 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 is a library first: not a chat app, not a coding-agent harness, not a multi-agent orchestrator.

Reach for Reigner when you want a Q&A agent shaped to your corpus and use case — not a fixed product. It's a library you tune end to end: the instructions, the tools, the schema, the model are all yours to change. Within that flexibility it keeps two things 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 / 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 endpoint that streams over Server-Sent Events (SSE), so your apps consume it with no rewrite. (MCP — Model Context Protocol — 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.
• Oracle escalation — a cheap default model runs the loop and calls escalate_to_oracle only when a question needs deeper reasoning; that single turn is served by a stronger model, then it reverts. You pay frontier prices for the hard steps, not the routine ones.
• Context stays bounded under pressure — a legible agent loop with numbered guardrails: pressure-driven history compaction, per-tool result truncation, and a scratchpad whose notes survive compaction — so long runs don't blow the context budget.
• Parallel, cached reads — read-only tool calls in a turn run concurrently and hit a per-session cache, cutting both latency and duplicate model-driven calls.
• An eval battery — score your agent against your own compiled corpus for faithfulness, coverage, and repeated-call efficiency; iterate on REIGNER.md, tools, or model with numbers.
• Forkable sessions — durable JSONL sessions on disk that you can fork and replay to compare variants without re-running from scratch.
• Write your UI once — the CLI, HTTP server, and MCP all emit the same typed events (tool calls, citations, status), so an interface, logger, or integration you build against one surface works unchanged against the others.
• 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.
• Architecture — the harness: agent loop, oracle escalation, and the G1–G11 context guardrails.
• Observability — OpenTelemetry spans for the agent loop.
• Principles — why Reigner exists and 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.