Isnād–Rijāl Framework

Grade the narrators, not just log them. Claim-level provenance for multi-agent knowledge systems — adapted from classical hadith transmission science.

🌐 Full project home: https://alizahidraja.com/isnad

What & Why

In modern AI pipelines, a factual claim passes through many hands — a scraper extracts it, a model compiles it, another serves it. Each hand can drop, distort, or invent. Existing tools record what happened. ISNAD grades who transformed the claim, so it can tell you how much to trust the result.

The framework adapts hadith transmission science — one of history's most rigorous epistemologies, refined over twelve centuries — into a Python library for AI systems. Every claim carries its complete chain of transmitters (isnād); each transmitter is graded in a living registry (rijāl); chains are evaluated by their weakest link; content is criticized independently of transmission quality; and the two combine in a decision matrix that routes claims to serve, review, or quarantine.

60-Second Quickstart

pip install isnad

from isnad import Registry, Chain, ChainLinkSpec, grade_chain, decide
from isnad.types import NarratorGrade, ContentVerdict
from isnad.critics import EmbeddingCritic

# Build a chain: source → scraper → model
chain = Chain([
    ChainLinkSpec("openstax-textbook", 0, domain="physics"),
    ChainLinkSpec("pdf-scraper-v2", 1),
    ChainLinkSpec("ingest-model-v3", 2),
])

# Seed-grade known narrators (required for coverage — see §8 experiment)
reg = Registry()
reg.register("openstax-textbook", "physics", grade=NarratorGrade.RELIABLE)
reg.register("pdf-scraper-v2", "physics", grade=NarratorGrade.RELIABLE)
reg.register("ingest-model-v3", "physics", grade=NarratorGrade.ACCEPTABLE)

# Grade the chain
grades = [reg.get_grade(l.narrator_id, l.domain) for l in chain.links]
transforms = [l.transform_type for l in chain.links]
chain_grade = grade_chain(grades, transforms, is_complete=True)

# Content criticism (now functional — embedding-based)
critic = EmbeddingCritic()
verdict = critic.evaluate("p = h/λ", "p = h/lambda", ["p = mv"])
action = decide(chain_grade, verdict)

print(f"Chain: {chain_grade.value.upper()} | Content: {verdict.value} | Action: {action.value}")

LangChain Integration (5 lines)

pip install isnad[langchain]

from isnad.integrations.langchain import IsnadTracer, seed_registry
from isnad.critics import EmbeddingCritic

reg = seed_registry({"source:docs": "reliable", "model:gpt-4o": "acceptable"})
tracer = IsnadTracer(registry=reg, critic=EmbeddingCritic())
chain.invoke("What is F=ma?", config={"callbacks": [tracer]})
print(tracer.report())

What's Validated vs. What's Not

The honesty box is a feature. We tell you exactly what works, what's limited, and where you need to supply your own components.

Concept → Module Map

The Decision Matrix

Pluggable Strategies — Extend It

The framework leaves key parameters open by design (paper §4.2/§4.3). Swap any:

Swap a critic in one line:

from isnad.critics import EmbeddingCritic, LLMCritic

critic = EmbeddingCritic()                            # offline, fast
critic = LLMCritic(api_key="sk-...")                  # LLM-backed, higher quality

Good first issues:

• Implement an alternative critic (sentence-transformers embedding, CrewAI integration)
• Seed-grade bootstrapper from published benchmark data
• Corroboration on a warm-grade corpus (the §8 experiment showed it's gated on warm grades)

Ecosystem

• 🌐 Site: https://alizahidraja.com/isnad
• 📄 Paper (DOI): https://doi.org/10.5281/zenodo.21211291
• 💾 Software (DOI): https://doi.org/10.5281/zenodo.21216873
• 📦 PyPI: https://pypi.org/project/isnad/
• 📝 Companion gist: https://gist.github.com/alizahidraja/56beaadf493976182f38aa602b8958e2
• 🧪 §8 Experiment & results: experiments/s8_gated_vs_ungated/
• 🔌 LangChain integration: src/isnad/integrations/langchain/
• 📊 Critic evaluation: src/isnad/critics/CRITIC_EVAL.md

Citation

@article{raja2026grading,
  author  = {Ali Zahid Raja},
  title   = {Grading the Narrators: An Isnād–Rijāl Framework for
             Claim-Level Provenance in Multi-Agent Knowledge Systems},
  year    = 2026,
  doi     = {10.5281/zenodo.21211291},
}

@software{raja2026isnad,
  author  = {Ali Zahid Raja},
  title   = {Isnād–Rijāl Framework: Reference Implementation},
  year    = 2026,
  doi     = {10.5281/zenodo.21216873},
  orcid   = {0009-0003-7875-4590},
}

About

Built by Ali Zahid Raja · ORCID 0009-0003-7875-4590

The rigor belongs to twelve centuries of muḥaddithūn. The transfer to AI systems is the contribution claimed here. Built in public — collaborators welcome.

License: Code — Apache 2.0 · Paper & docs — CC BY 4.0