ISNAD
Claim-level trust for multi-agent AI systems.
Every claim carries its chain. Every transmitter is graded. Adapted from 1,200 years of hadith transmission science.
Every claim carries its chain.
State: sound chain | hover/tap nodes for details
The Problem
When a multi-agent AI system answers you, that claim has passed through many hands — a source, a scraper, an ingestion model, a synthesis model. Each hand can drop, distort, or invent.
Provenance tools record what happened. Nothing grades who did it — or tells you how much to trust the result. A confident model at the end of a chain cannot repair a corrupted extraction at the start of it.
The Idea
Classical Islamic hadith science spent twelve centuries on a structurally identical problem: whether to trust knowledge transmitted through chains of human narrators. ISNAD transfers that methodology to AI pipelines.
Every claim carries its transmission chain (isnād)
Every transmitter keeps a living, per-domain grade (rijāl)
The weakest link caps the chain's trust
Independent corroboration upgrades — capped and gated (mutābaʿāt)
Content is criticized separately from the chain (matn)
The Decision Matrix
| Chain Grade | Content Consistent | Contradiction Detected |
|---|---|---|
| Ṣaḥīḥ-tier | Serve directly | ʿIlal flag → human review |
| Ḥasan-tier | Serve with caveat | Hold — review queue |
| Ḍaʿīf-tier | Hold — seek corroboration | Quarantine |
| Mawḍūʿ-tier | Reject & quarantine narrator | Reject & quarantine narrator |
Chain grade × content criticism → action. A sound chain carrying contradicted content is the system's most informative signal, not an error.
Quickstart
git clone https://github.com/alizahidraja/isnad && cd isnad make install && make demo
pip install isnad from isnad import Registry, Chain, grade_chain, decide # build a chain, grade it, route it through the matrix
Honest Status
What this is
A reference implementation of the framework's architecture and logic — 90 tests, the paper's worked example running end-to-end, five pluggable strategy interfaces.
What it is not yet
The end-to-end empirical validation that narrator grading reduces served errors. That experiment is specified in the paper (§8) and is being built in public. Collaborators welcome.
Links
Cite
@misc{raja2026isnad,
author = {Raja, Ali Zahid},
title = {Grading the Narrators: An Isnād–Rijāl Framework for
Claim-Level Provenance in Multi-Agent Knowledge Systems},
year = {2026}, month = jul,
doi = {10.5281/zenodo.21211291},
url = {https://doi.org/10.5281/zenodo.21211291}
}