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langchain - 💡(How to fix) Fix [Community] Add AgenticReasoningLoader [3 comments, 2 participants]

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langchain-ai/langchain#36019•Fetched 2026-04-08 00:52:58
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SaschaDeforthSaschaDeforth
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Code Example

pip install git+https://github.com/975SDE/langchain-arp.git
RAW_BUFFERClick to expand / collapse

Checked other resources

  • This is a feature request, not a bug report or usage question.
  • I added a clear and descriptive title that summarizes the feature request.
  • I used the GitHub search to find a similar feature request and didn't find it.
  • I checked the LangChain documentation and API reference to see if this feature already exists.
  • This is not related to the langchain-community package.

Package (Required)

  • langchain
  • langchain-openai
  • langchain-anthropic
  • langchain-classic
  • langchain-core
  • langchain-model-profiles
  • langchain-tests
  • langchain-text-splitters
  • langchain-chroma
  • langchain-deepseek
  • langchain-exa
  • langchain-fireworks
  • langchain-groq
  • langchain-huggingface
  • langchain-mistralai
  • langchain-nomic
  • langchain-ollama
  • langchain-openrouter
  • langchain-perplexity
  • langchain-qdrant
  • langchain-xai
  • Other / not sure / general

Feature Description

New Community Document Loader

Package: langchain-arp
Repository: https://github.com/975SDE/langchain-arp
License: MIT
Python: 3.9+

What is reasoning.json?

The Agentic Reasoning Protocol (ARP) is an open web standard that provides machine-readable reasoning directives, anti-hallucination guardrails, and counterfactual logic to autonomous AI agents.

A reasoning.json file is served at /.well-known/reasoning.json — similar to how robots.txt controls crawlers and schema.org describes entities, reasoning.json teaches AI agents how to think about an entity.

Use Case

Why this matters for LangChain

As AI agents increasingly reason about brands and entities, hallucination prevention becomes critical. This loader gives LangChain developers a standardized way to ingest entity-approved ground truth into their RAG pipelines — reducing hallucination rates for specific entities.

Proposed Solution

No response

Alternatives Considered

N/A - Standard implementation for a new protocol.

Additional Context

Installation

pip install git+https://github.com/975SDE/langchain-arp.git

Specification

Author: Sascha Deforth (@975SDE)
Website: https://arp-protocol.org

extent analysis

Fix Plan

To implement the Community Document Loader for reasoning.json in LangChain, follow these steps:

  • Install the langchain-arp package using pip:
pip install git+https://github.com/975SDE/langchain-arp.git
  • Create a reasoning.json file at the /.well-known/reasoning.json path, following the ARP protocol specification and JSON schema.
  • Implement a loader function in LangChain to ingest the reasoning.json file, using the langchain-arp package:
import json
from langchain_arp import ReasoningLoader

def load_reasoning_json(url):
    # Load the reasoning.json file from the given URL
    response = requests.get(url)
    data = json.loads(response.content)
    
    # Create a ReasoningLoader instance
    loader = ReasoningLoader(data)
    
    # Use the loader to ingest the reasoning.json data into LangChain
    return loader.load()
  • Integrate the loader function into the LangChain pipeline to reduce hallucination rates for specific entities.

Verification

To verify that the fix worked, test the loader function with a sample reasoning.json file and check that the data is correctly ingested into LangChain. You can also monitor the hallucination rates for specific entities to ensure that they are reduced after implementing the loader.

Extra Tips

  • Make sure to follow the ARP protocol specification and JSON schema when creating the reasoning.json file.
  • Test the loader function thoroughly to ensure that it works correctly with different types of reasoning.json files.
  • Consider implementing additional error handling and logging mechanisms to ensure that any issues with the loader function are properly handled and reported.

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#api#ssr#installation#tensor shape#autograd error#latency issue#model loading

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langchain - 💡(How to fix) Fix [Community] Add AgenticReasoningLoader [3 comments, 2 participants]