langchain-arp
LangChain Document Loader for
reasoning.json— the Agentic Reasoning Protocol
Load brand reasoning directives, hallucination corrections, and counterfactual logic into any LangChain-powered AI agent with 3 lines of code.
Installation
Install directly from GitHub:
pip install git+https://github.com/975SDE/langchain-arp.git
With optional LangChain integration:
pip install "langchain-arp[langchain] @ git+https://github.com/975SDE/langchain-arp.git"
With JSON Schema validation:
pip install "langchain-arp[all] @ git+https://github.com/975SDE/langchain-arp.git"
Note: This package is installed directly from GitHub. PyPI publishing is planned for a future release.
Quick Start
from langchain_arp import AgenticReasoningLoader
# Load from the ARP protocol website (dogfooding!)
loader = AgenticReasoningLoader("https://arp-protocol.org")
docs = loader.load()
# Load from a local file
loader = AgenticReasoningLoader("./reasoning.json")
docs = loader.load()
# Each section becomes a separate Document
for doc in docs:
print(f"[{doc.metadata['section']}] {doc.page_content[:100]}...")
How It Works
The loader fetches /.well-known/reasoning.json from any website and converts it into prioritized LangChain Documents:
| Priority | Section | Purpose |
|---|---|---|
| 1 (highest) | Corrections | Prevents AI hallucinations with false_claim → correction_fact pairs |
| 2 | Identity | Brand facts, tagline, competencies, system instruction |
| 3 | Recommendations | When to recommend / when NOT to recommend this entity |
| 4 | Counterfactuals | Pre-programmed "what-if" reasoning scenarios |
| 5 | Dichotomies | Competitive positioning logic |
| 6 | Authority | Trust signals, certifications, official links |
Use in a RAG Pipeline
from langchain_arp import AgenticReasoningLoader
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
# Load reasoning directives from any website with reasoning.json
loader = AgenticReasoningLoader("https://arp-protocol.org")
docs = loader.load()
# Add to vector store — your agent now knows how to reason about this brand
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
Standalone Usage (No LangChain Required)
from langchain_arp import load_reasoning, load_reasoning_file
# From URL
docs = load_reasoning("https://arp-protocol.org")
# From file
docs = load_reasoning_file("./reasoning.json")
CLI
python -m langchain_arp.loader https://arp-protocol.org
python -m langchain_arp.loader ./reasoning.json
What is reasoning.json?
reasoning.json is an open standard (MIT licensed) that provides machine-readable reasoning directives to AI agents. It lives at /.well-known/reasoning.json on any website.
While robots.txt tells crawlers what to access and schema.org tells machines what things are, reasoning.json teaches AI how to think about an entity — including corrections for known hallucinations, competitive positioning logic, and recommendation boundaries.
Spec: arp-protocol.org | Schema: v1.json | GitHub: 975SDE/arp-protocol
License
MIT — Sascha Deforth / TrueSource
Metadata
Release files for langchain-arp 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| langchain_arp-0.1.0.tar.gz | 10.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| langchain_arp-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.1 kB
Release files / langchain_arp-0.1.0.tar.gz
| Download URL | langchain_arp-0.1.0.tar.gz |
|---|---|
| Size | 10.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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Release files / langchain_arp-0.1.0-py3-none-any.whl
| Download URL | langchain_arp-0.1.0-py3-none-any.whl |
|---|---|
| Size | 9.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
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