Refract

Open infrastructure for agent-readable knowledge change. Turns source histories into replayable semantic change events.

v0.5.0+: Events now carry 6 deterministic enrichment fields (editMagnitude, directionSignal, certaintyProfile, etc.) — byte-reproducible, no model.

What does it do?

Given a Wikipedia page, Refract produces a structured event stream showing what changed, when, and how — every sentence that appeared, was removed, or was modified; every citation that shifted; every revert and edit cluster.

npx @refract-org/cli analyze "Climate change" --brief

Output:

sentence_first_seen  | 2026-01-15 | "Climate change has led to increased frequency of extreme weather events."
sentence_modified    | 2026-03-02 | "Climate change has intensified..." (edit magnitude: 0.31)
citation_added       | 2026-03-02 | [IPCC AR6 Report] → paragraph 4
sentence_removed     | 2026-04-10 | "Some researchers dispute..." (reverted next edit)
revert               | 2026-04-11 | Full revert of 2026-04-10 edit (edit cluster: 3 edits in 2h)

No model. No API. Byte-reproducible — the same source produces the same events every time.

Refract ingests versioned sources (MediaWiki, text files), computes structural and semantic change events, tracks claims and citations across time, and emits structured provenance data that downstream systems can query, replay, and audit.

Most knowledge systems answer what does this source say now? Refract helps answer: what changed, when, where, and what did the record say at a specific point in time?

Built and maintained by NextConsensus and Kanav Jain. Domain-neutral — Refract observes change, applications interpret relevance.

Repository boundary

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Quick start

Prerequisites

• Node.js 20+ or Bun 1.3+
• A MediaWiki URL (Wikipedia, Wikibooks, any public wiki)

One-shot analysis

# Using npx (no install required)
npx @refract-org/cli analyze "https://en.wikipedia.org/wiki/Artificial_intelligence" --brief

# Or install globally
npm install -g @refract-org/cli
refract analyze "Quantum computing" --depth forensic --format ndjson

Using the Python SDK

pip install refract-py

from refract import Refract

r = Refract()
events = r.analyze("Bitcoin", depth="brief")
for event in events:
    print(event.eventType, event.timestamp)

# As a DataFrame
df = r.analyze("Ethereum", depth="forensic", as_frame=True)
print(df.groupby("event_type").size())

From source

git clone https://github.com/refract-org/refract.git
cd refract
npm install
npx refract analyze "https://en.wikipedia.org/wiki/Machine_learning" --brief

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What Refract captures

Refract emits 26 deterministic event types from versioned sources:

Category	Events
Appearance	sentence_first_seen, sentence_removed, sentence_modified, sentence_reintroduced
Citations	citation_added, citation_removed, citation_replaced
Templates	template_added, template_removed, template_parameter_changed
Sections	section_reorganized, lead_promotion, lead_demotion
Reverts	revert_detected, edit_cluster_detected
Links & categories	wikilink_added, wikilink_removed, category_added, category_removed
Page metadata	page_moved, protection_changed
Talk page	talk_page_correlated, talk_thread_opened, talk_thread_archived, talk_reply_added, talk_activity_spike

Who This Is For

• Investigative journalists — trace how a claim about a public figure evolved across revision history: when it was added, who softened it, when sources appeared or disappeared
• Wikipedia editors — audit how policy templates (NPOV, BLP, due-weight) correlate with content changes over time
• Data scientists & researchers — deterministic features for edit-quality models, sourcing-behavior studies, content-drift measurement
• OSINT analysts — structured event streams from public editorial history, reproducible on request
• Fan wiki communities — canon disputes, headcanon drift, content-fork detection across MediaWiki instances
• AI/ML teams — training data curation (include stable, well-sourced claims; exclude contested or source-fragile ones), provenance-aware RAG, temporal leakage detection in training corpora
• Regulatory & compliance monitors — track changes to drug safety pages, guideline entries, and policy language for early signals of institutional shifts
• Knowledge graph engineers — extract entity and relationship changes across revision history for evolving ontologies
• Publishers & platform trust teams — monitor how claims spread, get cited, and stabilize across the public record

What Downstream Systems Build

Each consumer brings their own interpretation layer on top of Refract's deterministic event stream:

Consumer	They build	How they use Refract
Healthcare intelligence	Sourced briefs answering "does this claim still hold up?"	Feed structured events into a 4-lane measurement pipeline (clinical truth, ratification, economic stake, feasibility). Each event carries FactProvenance with the exact thresholds used.
AI training data curation	Training datasets filtered by claim stability	Score each claim by revert count, citation churn, talk page correlation, and template dispute history from the event stream. Include only claims above a stability threshold.
Provenance-aware RAG	Retrieval that weights results by claim stability	Enrich each retrieved chunk with its claim history — stable, recently changed, source-fragile, contested. The RAG system uses the signal to filter or demote low-confidence results.
Regulatory monitoring	Early-warning dashboards for policy changes	Run refract cron on drug pages, guideline entries, and regulatory topics. When new events fire (citation removal, template dispute, section reorganization), alert the monitoring team with the structured diff.
Competitive intelligence	Cross-jurisdiction claim divergence maps	Use refract diff to compare the same topic across wikis (English vs German Wikipedia, Fandom vs independent wiki). Track how framing differs and when it diverged.
Fact-checking	Claim provenance timelines	Given a claim text, query its lifecycle across the event stream — first appearance, source additions, revert history, talk page correlation, stabilization time. Return a verifiable timeline.
Academic research	Large-scale knowledge dynamics studies	Export ObservationReport with Merkle-verifiable claim histories. Run cohort analyses on claim stability across topics, time periods, and editorial environments.
Journalism forensics	Edit pattern analysis for public figures	Track how a specific claim about a person or topic evolved. Look for coordinated editing, source softening, or removal without replacement.
Fan wiki canon tracking	Canon divergence detection across competing wikis	Compare the same fictional universe's page across Fandom and independent wikis. Detect when one wiki retcons content while the other doesn't — and by how much.
Knowledge graph engineering	Evolving ontologies from category and link changes	Use refract analyze --depth forensic to capture category_added/removed and wikilink_added/removed events. Build an entity graph that evolves with the public record.

The common architecture: Refract extracts the mechanical facts. The downstream system interprets what those facts mean for its domain. No interpretation enters Refract's pipeline; no consumer re-extracts from raw revision history.

Why Refract Over Raw Wikipedia API?

You could write a script that calls the Wikipedia API and parses diffs. Many people do. Here is what Refract gives you that a raw script does not:

Raw API script	Refract
You parse revision diffs	26 deterministic event types, already classified
You track citations manually	Citation add/remove/replace with source lineage
You guess at section changes	Section-aware diffs with movement, promotion, demotion
You detect reverts by pattern matching	6 regex patterns + edit cluster detection
You correlate talk pages by hand	Automatic talk-page correlation and activity spikes
Your output format is ad-hoc	Standardized JSON/NDJSON with deterministic event IDs
You write your own replay logic	Replay manifests with Merkle proofs for auditability
You maintain your own Wikipedia client	Rate limiting, retries, auth, pagination — built in
Your results change when you re-run	Byte-reproducible — same input, same output every time
You build your own timeline	Timeline builder with claim lifecycle tracking

Refract is not magic. It is the 800+ lines of brittle Wikipedia analysis code you would otherwise write and maintain, packaged as a deterministic, versioned, testable engine. See examples/04-from-scratch-to-refract.ts for a side-by-side comparison.

Complementary technologies

Refract pairs naturally with modern tools. The event stream is standard NDJSON — anything that reads JSON or speaks HTTP can consume it. See the integrations docs for full details.

Category	Technology	How they fit
Python / data science	pandas, Jupyter, matplotlib	refract-py wraps the CLI and exports typed DataFrames. Tutorial
Vector databases	Pinecone, Weaviate, pgvector	Store claim embeddings alongside stability metadata. Query: "find claims like X that are stable and well-sourced."
RAG frameworks	LangChain, LlamaIndex	Use Refract's stability signals as retrieval filters or reranking features. LangChain loader available in refract-py.
AI coding agents	Claude Code, Cline, Codex CLI, OpenClaw	Agents connect via Refract's built-in MCP server (refract mcp) to read claim history and cite provenance.
MCP (Model Context Protocol)	Any MCP client	refract mcp is a native MCP server with tools for analyze, claim, export, cron, and classify.
Data query	DuckDB, ClickHouse	Query NDJSON output with SQL: SELECT event_type, count(*) FROM 'events.jsonl' GROUP BY event_type;
Monitoring	Slack, Email, Webhooks, GitHub Actions	refract cron + --notify-slack for scheduled monitoring with alerts. Tutorial
Streaming	Kafka, Redpanda, Cloudflare Queues	Each EvidenceEvent is a message keyed by claimId for real-time monitoring.
Visualization	Observable Framework, Mermaid, D3	refract visualize --format mermaid produces Mermaid diagrams. Observable has a @refract-org/observable data loader.
Model serving	OpenAI API, DeepSeek, Ollama, vLLM, Workers AI	Any OpenAI-compatible endpoint plugs into refract classify. Workers AI runs at the edge.
Local inference	WebGPU, MLX, llama.cpp	Run detection models on-device — no API key needed. Refract defaults are mechanical; any boundary can use a local model via Ollama or MCP sampling.
Notebooks	Jupyter, Marimo, Observable	Load events into a DataFrame: pd.read_json("events.jsonl", lines=True). Marimo's reactive runtime is ideal for live event stream analysis.
Serverless	Cloudflare Workers, D1, R2	Run refract via npx in a Worker. Store events in D1, export to R2, queue re-observations. Entirely edge-deployable.

Quick Start

The fastest way to understand what Refract does:

# 1. Run the guided onboarding (recommended first step)
npx @refract-org/cli init

# 2. Analyze any page
npx @refract-org/cli analyze "Bitcoin" --depth brief

# 3. Open the local web explorer (starts a server at localhost:8899)
npx @refract-org/cli explore "Bitcoin"

# 4. Export as JSON for your own tools
npx @refract-org/cli analyze "Bitcoin" --json | jq .events[0]

# 5. Export as structured data
npx @refract-org/cli export "Bitcoin" --format ndjson > bitcoin-events.ndjson

What you're seeing: Refract reports deterministic, reproducible claim and citation changes. It does not decide whether a change is true or important — that's for downstream applications.

Install once, run fast: npx downloads on every run (~20s cold start). For repeated use: bun add @refract-org/cli (install once, refract command available instantly).

Other install options

Method	Command
Bun (if installed)	bunx @refract-org/cli analyze "Bitcoin"
Bun (one command)	bunx @refract-org/cli analyze "Bitcoin"
Local install	bun add @refract-org/cli && refract analyze "Bitcoin" (or wikihistory)
Build from source	git clone https://github.com/refract-org/refract && cd refract && bun install && bun run build

Use individual packages

bun add @refract-org/evidence-graph @refract-org/analyzers

import type { EvidenceEvent, Revision } from "@refract-org/evidence-graph";
import { sectionDiffer, citationTracker } from "@refract-org/analyzers";

Packages

Package	npm	Description
@refract-org/evidence-graph		Core types, schemas, BYO-inference boundaries
@refract-org/ingestion		Wikimedia API adapters — fetching, diffing, rate limits
@refract-org/analyzers		Deterministic analyzers — sections, citations, reverts, templates
@refract-org/cli		CLI tool — refract / wikihistory commands, classify inference
@refract-org/persistence	—	Local SQLite persistence (bun:sqlite, not published)
@refract-org/eval		Evaluation harness — ground truth validation and benchmarks
@refract-org/observable	—	Observable Framework data loader (not published)

How It Compares

Refract tracks claim provenance — structured evidence linking a claim's lifecycle to specific revisions, sources, and policy signals. It complements existing tools:

Tool	What it does	What Refract adds
WikiWho	Sentence-level authorship (who wrote which token)	Sentence lifecycle: when a sentence first appeared, was removed, rewrote, or was reintroduced
ORES	ML edit quality scores (likely damaging, good-faith)	Deterministic edit classification + policy-coded signals
XTools	Edit stats, page history summaries, top editors	Structured event stream: section changes, citation turnover, template diffs, page moves, category shifts
PetScan	Category intersection queries across pages	Category evolution per-page over time

Architecture

The engine follows a two-knowledge-split:

1. Deterministic: Wikipedia API ingestion, diff computation, section extraction, citation tracking, template classification, revert detection — byte-reproducible, no model involved.
2. Outcome labels: Independently sourced ground truth (talk page consensus, page protection events) — never redefined by the pipeline.

Full architecture

Configurable heuristics / BYO-inference boundaries

Every analyzer threshold is a typed function signature where a model can replace the default heuristic. The defaults work offline with no configuration required:

Boundary	Default (mechanical)	Plug in a model to decide
Sentence similarity	Word-overlap ratio (0.8)	"Are these two sentences the same claim?"
Revert detection	6 regex patterns	"Is this edit comment a revert?"
Template classification	Name-to-type lookup	"What policy signal does this template represent?"
Edit cluster detection	Time window + min size	"Are these edits semantically related?"
Heuristic classification	Size thresholds + comment patterns	"What kind of edit is this?"

Pass overrides via --config file or inline CLI flags (--similarity, --spike-factor, --cluster-window, etc.). The effective parameters are recorded in each event's FactProvenance.parameters when non-default values are used.

# Domain-tuned: Fandom wikis have tighter edit clusters
refract analyze "Darth_Vader" --api https://starwars.fandom.com/api.php --cluster-window 30 --similarity 0.85

Private Instances

Refract connects to any MediaWiki instance — corporate wikis, institutional knowledge bases, private fan wikis. Use the --api flag with the wiki's api.php URL.

Authentication

Method	CLI flags	Description
Bearer token	--api-key <token>	Sends Authorization: Bearer <token> with every request
Basic auth	--api-user <user> --api-password <pass>	Sends HTTP basic auth credentials
OAuth2	OAUTH_CLIENT_ID + OAUTH_CLIENT_SECRET env vars	Sends X-OAuth-Client-Id and X-OAuth-Client-Secret headers

All three methods work with every command:

# Bearer token
refract analyze "Page" --api https://corp-wiki.example.com/w/api.php --api-key "sk-..."

# Basic auth
refract analyze "Page" --api https://corp-wiki.example.com/w/api.php --api-user "admin" --api-password "..."

# OAuth2 (via env vars)
OAUTH_CLIENT_ID="..." OAUTH_CLIENT_SECRET="..." \
  refract analyze "Page" --api https://corp-wiki.example.com/w/api.php

Credentials are never logged or exposed in error messages.

Local Docker Testing

A Docker Compose setup is available for testing against a local MediaWiki instance with auth:

cd docker
docker compose up -d
DOCKER_TESTS=true bun run test

This starts MediaWiki at http://localhost:8080 and an nginx proxy with basic auth at http://localhost:8081. The auth integration tests validate bearer token, basic auth, and OAuth2 paths.

Beyond Wikipedia

Refract works on any public MediaWiki instance — Fandom.com, independent fan wikis, private wikis. Wikipedia's editorial norms suppress the most interesting dynamics; fandom wikis don't.

Dynamic	What Refract captures
Canon disputes	category_removed: Canon characters → category_added: Legends characters after the 2014 Disney acquisition
Headcanon drift	"Vader turned because of fear of loss" vs "pride and ambition" — reversibly edited, both cite the same films
Warring wikis	Cross-wiki diff detects a Game of Thrones Fandom wiki fork vs parallel evolution on an independent ASOIAF wiki
Decade-spanning consensus	2008 talk page consensus about what's canon, overturned in 2023 — L3 outcome labels with temporal validity windows

If the engine handles fandom, it handles anything.

Why It Exists

Machines do not just need more retrieved text. They need provenance, instability, disagreement, and temporal change — six things that a current snapshot cannot provide:

1. Where it appeared — when a claim first entered the public record
2. How it changed — every addition, removal, reintroduction, and in-place modification
3. What was tagged — policy templates, dispute signals, maintenance markers
4. What was reverted — every revert, edit cluster, concentrated contestation
5. What moved — section reorganization, lead promotion, category shifts, page moves
6. What was discussed — correlated talk page activity, thread lifecycle, activity spikes

Refract makes that knowledge legible to machines by decomposing every statement into its history. More durable than search, monitoring, or summarization.

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What It Is Not

Category	Why
Truth detector	Reports what changed, not whether the change is accurate
Model interpreter	No LLM in the pipeline — interpretation lives downstream in consumers
Editor quality judge	No scoring, ranking, or profiling of individual editors
Prediction engine	No forecasting, no sentiment analysis, no trend extrapolation
Live monitor	Polling-based, not real-time — use cron mode for scheduled observation
Healthcare scorer	Domain-agnostic by design — no clinical, regulatory, or payer logic

License

AGPL-3.0. See LICENSE.

If you modify this software and deploy it as a network service, you must release your modifications.

Commercial use: NextConsensus offers commercial licenses for proprietary integration without AGPL obligations. See nextconsensus.com.

Community

• Contributing — how to get started
• Good first tasks — ready-to-pick-up work items
• Discussions — questions, ideas
• Code of Conduct
• Security
• Changelog
• Cite this software

Ecosystem

These repos extend the core engine:

Repo	Purpose
refract-docs	Public documentation site — integrations, quickstart, CLI, SDK, tutorials
refract-labs	Experimental probes applying the engine to adjacent verticals
refract-ui	Standalone visualization — load JSONL, render timelines, diffs, citations
refract-demo-data	Safe, fictional datasets for the eval harness (no real PII or medical data)
refract-py	Python SDK — typed dataclasses and pandas integration for ML workflows

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