Our mission is to help people and organizations make claims, evidence and corrections inspectable, so decisions can be revisited when new facts emerge.
Comuvia helps developers preserve what was said, which evidence supported it, and how a later evaluation was calculated. Use the offline comuvia library to build portable records. Add foreglass when you want to read supported public ForeGlass artifacts.
Start with the purpose: How evidence, dependencies and data quality work uses an article and a statistical table to explain the value for readers, media and organizations.
Run an offline example
Save a synthetic forecast, score its outcome and reproduce the result. Requires pip install comuvia; no API key and no network access for the offline example.
Preserve a claim
Separate a source statement from your interpretation and keep corrections visible.
PROVIDER DATARead ForeGlass
Retain exact source bytes, inspect available lanes and identify what cannot be evaluated.
What you can build
| Your workflow | How the SDK helps | What you still provide |
|---|---|---|
| Research or media evidence trail | Link statements to retained source material; append interpretations and corrections | Source rights, extraction, editorial judgment |
| Forecast evaluation | Pin questions, declared probabilities, outcomes and a reproducible binary scoring rule | Properly defined questions, information cutoffs and outcome evidence |
| Agent or investment research integration | Read supported ForeGlass data, retain caveats and report missing metadata | Your application, decision policy and independent validation |
| Simulation publication | Record model artifacts and conditional estimates without passing them off as observed facts | Model, data provenance and assumptions you are permitted to disclose |
For a reader, this means a clearer route from a number to its evidence. For an organization, it makes handoffs and corrections easier to audit. The SDK provides a common record layer; the value depends on the quality of the evidence and the workflow using it.
Two packages, one record layer
Available in 0.1.0
Both packages are published on PyPI as version 0.1.0 (released 2026-10-01). The implemented core includes nine record types, strict JSON validation, canonical content identity, a single-writer append-only store, the binary-brier-v1 rule and evaluation reproduction. The optional client reads an allowlisted set of public artifacts.
Published 2026-10-01. comuvia 0.1.0 and foreglass 0.1.0 are on PyPI, built and uploaded by the public repository's release workflow with published attestations. These guides describe that release. 0.1.x is an alpha, so pin the version. See release status, verification and installation.
Boundaries that matter
- A matching digest establishes content identity against an expected digest. It does not establish truth, authorship or independent publication time.
- Extraction confidence and event probability are different fields. An LLM's confidence in reading an article is never substituted for a forecast probability.
- A conditional simulation is an experiment under assumptions. It is not automatically an eligible forecast.
- Named exclusions are useful results: they tell you which metadata or evidence is missing.
- The libraries do not host a registry, produce economic forecasts, run an MCP server or provide a universal credibility score.
Continue with the quickstart or compare record concepts.