CitableHub
CitableHub
Answer-first summary52/100 GEO

LangChain

Framework for LLM applications.

AI FrameworksFair citabilityVisit site →

Your Citability Score

52/100
Identity75/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
  • Identity: Add: Founder / Team, Social links.
  • Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
  • Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
  • Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
  • Classification: Add: At least 2 tags.

Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.

Frequently asked questions

What is LangChain?
Framework for LLM applications.
What does LangChain do?
Open-source framework for building applications with large language models through composable chains and agent patterns.
Who is LangChain for?
Developers and engineering teams building production LLM applications and AI agents.
Is LangChain verified?
LangChain is listed on CitableHub with a citability score of 52/100, computed from verifiable profile evidence.
LangChain logo

LangChain

Invited

Framework for LLM applications.

AI FrameworksCH-VER-967278Listed September 10, 2026
Visit Website
AI-Extractable Summary
What:Framework for LLM applications.
For whom:Developers and engineering teams building production LLM applications and AI agents
Key outcome:Teams can build, test, and deploy LLM-powered applications faster with reusable components for orchestration, retrieval, and tool use
Category:AI Frameworks

Structured for AI systems to extract and cite.

Citability Score

52/100
75
Identity
45
Evidence
15
Trust
50
Freshness
80
Classification
60
Impressions
0
Clicks
0
Likes
0
GQI Earned

Citable Outcome

Teams can build, test, and deploy LLM-powered applications faster with reusable components for orchestration, retrieval, and tool use.

About

Open-source framework for building applications with large language models through composable chains and agent patterns.

Target Audience: Developers and engineering teams building production LLM applications and AI agents.
Not ideal for: Non-technical users or teams looking for a no-code chatbot builder rather than a developer framework.

What makes it different

  • Composable primitives for chaining prompts, models, tools, memory, and retrieval
  • Strong ecosystem for RAG, agents, and integrations with vector stores and model providers
  • Supports rapid prototyping with a path to production-grade observability and evaluation
  • Large open-source community and extensive integration library for common AI workflows

Tags & Classification

rag applicationsai agentschatbotsdocument question answeringworkflow orchestration
software engineersml engineersstartup teamsenterprise dev teams
saasfintechhealthcareecommerce
Platform: FrameworkModel: Open Source

Links & Transparency

Website

Cite this Project

BibTeX
@misc{citablehub_langchain,
  title = {LangChain},
  url = {https://citablehub.com/p/langchain},
  note = {Listed September 10, 2026. CitableHub ID: CH-VER-967278},
  year = {2026}
}
APA
LangChain. (2026). CitableHub Software Index. https://citablehub.com/p/langchain.
MLA
"LangChain." CitableHub, 2026, https://citablehub.com/p/langchain.