What is LangChain? The Essential Framework for Building AI Applications

When developers first encountered ChatGPT in late 2022, they faced a frustrating reality: these powerful language models sat isolated, unable to remember conversations, access external data, or use tools. Building anything beyond a simple chatbot required weeks of custom infrastructure work. Harrison Chase, a machine learning engineer at Robust Intelligence, recognized this gap and released an 800-line Python package that would change everything. Within months, that small project became LangChain—now the industry standard for building AI applications, used by LinkedIn, Uber, and Klarna to power production systems serving millions of users.

TL;DR:

• LangChain is an open-source framework that simplifies building applications powered by large language models (LLMs) by providing modular, reusable components for prompts, memory, tools, and agents.

  
  

• Founded in October 2022 by Harrison Chase, LangChain raised $125 million in October 2025 at a $1.25 billion valuation, with 117,560+ GitHub stars and 130 million downloads.

  
  

• Powers production systems at major companies including Klarna (2.5 million conversations), LinkedIn (SQL Bot for employees), and Uber (automated code migrations).

  
  

• Solves critical LLM limitations by adding memory, external data access via Retrieval-Augmented Generation (RAG), tool integration, and multi-agent orchestration.

  
  

• Offers a complete ecosystem: LangChain (framework), LangGraph (agent orchestration), LangSmith (observability), and LangGraph Platform (deployment infrastructure).

LangChain is an open-source framework for developing applications powered by large language models. It provides modular components—prompts, chains, memory, agents, and retrieval systems—that developers combine like building blocks to create sophisticated AI applications. LangChain simplifies integrating LLMs with external data, tools, and complex workflows, making it possible to build production-ready chatbots, research assistants, code generators, and autonomous agents.

Bonus: AI in Business: Applications, Benefits & Implementation Guide

Bonus Plus: The Complete Guide to Physical AI: What It Is and Why It Matters

Bonus Plus Pro: AI Humanoid Robots: How They Work, Who's Building Them, and What's Next

Table of Contents

1. Understanding LangChain: Definition and Core Concepts

2. The Origin Story: How LangChain Started

3. Why LangChain Matters: Problems It Solves

4. Core Components: How LangChain Works

5. The LangChain Ecosystem

6. Real-World Applications and Use Cases

7. Case Studies: Companies Using LangChain

8. How to Build with LangChain: Step-by-Step Guide

9. Retrieval-Augmented Generation (RAG) Explained

10. Agents and Multi-Agent Systems

11. Pricing and Cost Structure

12. Pros and Cons

13. LangChain vs Alternatives

14. Common Myths and Misconceptions

15. Future Outlook and Trends

16. FAQ

17. Key Takeaways

18. Actionable Next Steps

19. Glossary

20. References

Understanding LangChain: Definition and Core Concepts

LangChain is a framework for developing applications powered by large language models. Created by Harrison Chase and launched as an open-source project in October 2022, LangChain provides a standardized interface and composable building blocks that developers use to construct complex AI applications (LangChain Documentation, 2025).

At its foundation, LangChain addresses a fundamental challenge: LLMs like GPT-4, Claude, and Gemini are powerful reasoning engines, but they lack persistent memory, cannot access real-time data, and have no ability to interact with external tools. LangChain bridges these gaps by providing:

Modular components that handle common patterns in LLM applications—prompt templates, conversation memory, document loaders, embedding models, vector stores, and agent frameworks.

A standard interface across different LLM providers, allowing developers to switch between OpenAI, Anthropic, Google, Mistral, Meta, and others without rewriting code.

Chain composition that links multiple operations together, enabling workflows like "retrieve relevant documents, then summarize them, then answer the user's question based on that summary."

Agent capabilities that let LLMs decide which tools to use and in what sequence to accomplish complex tasks.

The framework follows a chain-based sequential processing architecture. Each step in a chain takes input, performs an operation (calling an LLM, retrieving documents, executing code), and passes output to the next step. This modular design promotes flexibility and allows developers to build sophisticated applications by orchestrating components (Medium - Amit Patriwala, 2025).

LangChain saw explosive growth from its launch. From Q1 2024 to Q1 2025, the framework experienced a 220% increase in GitHub stars and a 300% increase in npm and PyPI downloads (InfoServices, April 2025). As of October 2025, LangChain has over 117,560 GitHub stars, more than 19,354 forks, and 28 million downloads per month (GitHub, 2025).

The Origin Story: How LangChain Started

Harrison Chase graduated from Harvard University with a degree in statistics and computer science. His professional journey took him through several influential roles: he led the entity linking team at Kensho, a fintech startup, then headed the machine learning team at Robust Intelligence, focusing on testing and validation of AI models (TED AI San Francisco, 2025).

While working at Robust Intelligence in 2022, Chase began experimenting with GPT-3 prototypes. He noticed a recurring frustration: friends building with language models were all implementing the same patterns—prompt management, conversation memory, document retrieval, tool integration—but everyone was writing these from scratch. This redundancy highlighted a need for abstraction (Latent Space Podcast, September 2023).

On October 24, 2022, Chase made the first commit to the LangChain repository. The initial release was a lightweight Python wrapper for prompt templates and basic chain composition, just 800 lines of code. He open-sourced it immediately under an MIT license (Wikipedia, August 2025).

The timing proved perfect. ChatGPT launched on November 30, 2022, igniting widespread interest in building LLM-powered applications. LangChain provided exactly what developers needed: a toolkit to move beyond simple chatbots. By January 2023, the project had gained enough traction that Chase incorporated LangChain as a company (Founder Story - Frederick AI, 2025).

Funding Timeline

April 2023: LangChain announced a $10 million seed round from Benchmark, immediately followed by a $20 million investment from Sequoia Capital at a valuation of at least $200 million (Wikipedia, August 2025).

February 2024: The company raised a $25 million Series A led by Sequoia Capital, bringing total funding to $55 million and valuing the company at $200 million (Contrary Research, February 2025).

October 2025: LangChain celebrated its third anniversary by announcing a $125 million funding round at a $1.25 billion valuation, cementing its status as a unicorn company (Bitcoin Ethereum News, October 2025).

The rapid fundraising reflected LangChain's explosive adoption. By October 2024, more than 132,000 LLM applications had been built using LangChain, with 4,000+ open-source contributors in its community and over 130 million total downloads across Python and JavaScript platforms (Contrary Research, February 2025).

Why LangChain Matters: Problems It Solves

Language models, despite their impressive capabilities, have critical limitations that prevent them from functioning as production-ready applications. LangChain emerged as the solution to six fundamental problems:

1. Memory and Context Management

LLMs are stateless by default. Each API call treats the conversation as brand new, with no memory of previous interactions. For applications like customer support chatbots or personal assistants, this creates an impossible user experience.

LangChain provides memory modules that track conversation history, tool outputs, and long-term user preferences. Developers can choose from several memory types: buffer memory (stores recent messages), summary memory (condenses long conversations), and vector memory (semantically searches past interactions). This lets applications maintain coherent, multi-turn conversations (InfoServices, April 2025).

2. Knowledge Cutoff and Outdated Information

LLMs are trained on fixed datasets with knowledge cutoffs. GPT-4 doesn't know about events after its training date. Claude lacks information about your company's internal documents. Fine-tuning is expensive and still doesn't solve the real-time data problem.

LangChain addresses this through Retrieval-Augmented Generation (RAG). Instead of relying only on the model's trained knowledge, applications can dynamically retrieve relevant information from external sources—databases, documents, APIs, wikis—and inject that context into the prompt. This keeps responses current and accurate without retraining the model (Python LangChain Documentation, 2025).

3. Tool Integration and Action Execution

LLMs generate text but cannot directly interact with the world. They can't send emails, query databases, run calculations, or call APIs—all actions that practical applications require.

LangChain's agent framework gives LLMs access to tools. An agent can reason about which tool to use, call it with appropriate parameters, observe the result, and decide the next action. This enables workflows like "search the database for customer data, calculate statistics, generate a summary, and send it via Slack" (DEV Community, September 2025).

4. Multi-Step Reasoning and Complex Workflows

Simple prompt-response patterns work for basic questions but fail for tasks requiring multiple steps, loops, conditional logic, or parallel execution.

LangChain's chain composition allows developers to build sophisticated workflows. Chains can be sequential (A→B→C), conditional (if A then B else C), or parallel (A and B simultaneously, then C). This structure makes it possible to implement research assistants that gather information, synthesize findings, verify facts, and generate reports (Medium - Aman Raghuvanshi, May 2025).

5. Provider Lock-In

When developers build directly against OpenAI's API, switching to Anthropic or Google requires rewriting significant code. Each provider has different interfaces, parameter names, and capabilities.

LangChain provides a unified interface across 25+ LLM providers. Code written for GPT-4 can run on Claude or Gemini by changing a single line. This portability protects against vendor lock-in and enables cost optimization by using different models for different tasks (Akka Blog, August 2025).

6. Production Readiness: Observability and Evaluation

Moving from prototype to production requires monitoring, debugging, and continuous evaluation. Without proper tooling, developers struggle to understand why their application misbehaves, track costs, or measure quality.

LangChain integrates with LangSmith, an observability platform that traces every step of execution, logs inputs and outputs, calculates token costs, and enables evaluation workflows. This visibility transforms the "black box" nature of LLM applications into analyzable, improvable systems (LangSmith AWS Marketplace, 2025).

Core Components: How LangChain Works

LangChain's architecture consists of several layers, each providing specific functionality:

1. LangChain-Core: Base Abstractions

The foundation layer defines interfaces for all components: chat models, embedding models, vector stores, document loaders, and output parsers. These abstractions ensure consistent behavior across different implementations (LangChain Documentation, 2025).

2. Integration Packages

Important integrations are split into lightweight, separately maintained packages:

• langchain-openai: OpenAI models (GPT-4, GPT-3.5, embeddings)

• langchain-anthropic: Claude models (Opus, Sonnet, Haiku)

• langchain-google: Gemini and PaLM models

• langchain-community: Third-party integrations maintained by the community

This modular approach keeps the core library lightweight while supporting hundreds of integrations (LangChain Documentation, 2025).

3. Prompt Templates

Prompt templates manage dynamic input formatting. Instead of manually concatenating strings, developers define templates with variable placeholders:

from langchain.prompts import PromptTemplate

template = """You are a helpful assistant. 
Use the following context to answer the question.

Context: {context}
Question: {question}
Answer:"""

prompt = PromptTemplate(
    input_variables=["context", "question"],
    template=template
)

Templates support conditional logic, few-shot examples, and chat message formatting (Latenode Blog, 2025).

4. Document Loaders and Text Splitters

LangChain provides loaders for diverse data sources: PDFs (PyPDF, pymupdf), web pages (CheerioWebBaseLoader), CSV files (CSVLoader), Google Drive documents, Notion pages, and more. Text splitters break large documents into chunks suitable for embedding and retrieval:

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = splitter.split_documents(documents)

The recursive splitter preserves semantic boundaries by trying to split on paragraphs first, then sentences, then words (Python LangChain RAG Tutorial, 2025).

5. Embeddings and Vector Stores

Embeddings convert text into numerical vectors that capture semantic meaning. LangChain integrates with embedding models from OpenAI, Cohere, Google, and Hugging Face.

Vector stores index these embeddings for fast similarity search. Supported databases include FAISS (local), Pinecone (cloud), ChromaDB (local/cloud), Weaviate (cloud), and Milvus (distributed). Developers can swap vector stores without changing application logic (Towards AI - Prasad Mahamulkar, November 2024).

6. Chains

Chains link components into workflows. The simplest chain passes an input through a prompt template, calls the LLM, and returns the result. Complex chains orchestrate multiple LLMs, retrievers, and custom functions:

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4", temperature=0)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever()
)

answer = qa_chain.run("What are the main findings?")

LangChain Expression Language (LCEL), introduced in Q3 2023, provides a declarative syntax for composing chains with streaming support and automatic parallelization (Wikipedia, August 2025).

7. Agents and Tools

Agents use LLMs to reason about which actions to take. Instead of following a predetermined sequence, agents:

1. Receive a goal or question

2. Decide which tool to use

3. Execute the tool and observe the result

4. Repeat until the goal is achieved or a stopping condition is met

Tools can be Python functions, API calls, search engines, calculators, or any executable action. LangChain provides built-in tools and makes it easy to define custom ones (Medium - Amit Patriwala, 2025).

The LangChain Ecosystem

What began as a single Python package has evolved into a comprehensive platform:

LangChain (Open-Source Framework)

The core open-source libraries (Python and JavaScript/TypeScript) remain free under MIT license. They provide the fundamental abstractions, integrations, chains, and agents. Development happens in the open on GitHub with contributions from thousands of developers (LangChain Documentation, 2025).

LangGraph

Launched in early 2024, LangGraph addresses limitations in the original LangChain chains. Chains work well for linear workflows but struggle with cyclic patterns, human-in-the-loop approval, and complex state management.

LangGraph represents applications as directed graphs where nodes are functions and edges define control flow. This structure enables:

• Cyclic workflows: Agents that can loop back to refine their approach

• Stateful execution: Persistent memory across long-running tasks

• Human-in-the-loop: Pause execution for human approval before proceeding

• Parallel execution: Run multiple branches simultaneously

Companies like LinkedIn, Uber, and Klarna use LangGraph for production agents (LangChain Blog - Building LangGraph, September 2025).

As of October 2025, the LangGraph GitHub repository has 19,982 stars, demonstrating rapid adoption since its 2024 launch (GitHub, October 2025).

LangSmith

LangSmith is a closed-source observability and evaluation platform that launched in February 2024. It provides:

Tracing: See every step of execution with input/output logs, timing data, and token counts.

Evaluation: Test applications on datasets, score outputs with LLM-as-Judge evaluators, and collect human feedback.

Prompt management: Version and collaborate on prompts with A/B testing support.

Cost tracking: Aggregate token costs across models and deployment environments.

As of February 2025, LangSmith has over 250,000 user signups, 1 billion trace logs, and more than 25,000 monthly active teams (Contrary Research, February 2025).

LangGraph Platform

Announced in May 2025 and reaching general availability in October 2025, LangGraph Platform provides managed infrastructure for deploying long-running, stateful AI agents. It handles:

• Horizontal scaling with task queues

• Persistent state management

• Streaming support for real-time responses

• Cron scheduling for periodic tasks

• APIs for deployment and monitoring

The platform offers three deployment models: fully managed cloud, hybrid (control plane in LangChain's cloud, data plane in your infrastructure), and self-hosted (Wikipedia, August 2025).

Real-World Applications and Use Cases

LangChain enables a broad spectrum of applications across industries:

Customer Support Automation

AI assistants handle customer inquiries by retrieving relevant information from knowledge bases, past conversations, and product documentation. They can escalate complex issues to human agents while resolving routine questions instantly. 60% of AI developers working on autonomous agents use LangChain as their primary orchestration layer (InfoServices, April 2025).

Document Analysis and Q&A

Legal firms, healthcare organizations, and financial institutions use LangChain-powered systems to analyze thousands of documents. Users ask natural language questions and receive answers with citations to source material. RAG pipelines retrieve relevant passages before generating responses, ensuring accuracy.

Code Generation and Migration

Developer tools built with LangChain assist with writing code, fixing bugs, generating tests, and performing large-scale refactors. Uber's Developer Platform team uses a LangGraph-based system to automate unit test generation during code migrations (LangChain Blog, January 2025).

Data Analysis and Business Intelligence

SQL generation agents convert natural language questions into database queries, execute them, and present results. LinkedIn's SQL Bot enables non-technical employees to independently access data insights by automatically finding the right tables, writing queries, and fixing errors (LangChain Blog, January 2025).

Research Assistants

Multi-agent research systems break complex queries into sub-questions, search multiple sources, synthesize findings, and generate comprehensive reports. These workflows involve planning, execution, and quality checking stages coordinated by LangChain agents.

Content Creation Pipelines

Marketing teams use multi-agent systems where one agent gathers research, another drafts content, and a third reviews for quality. LangChain's memory systems allow agents to share context, maintaining coherence across the pipeline (Latenode Blog, 2025).

Case Studies: Companies Using LangChain

Klarna: Customer Support at Scale

Challenge: Klarna, the Swedish fintech company with 85 million active users, faced growing challenges managing multi-departmental escalations in customer support. Rising consumer expectations demanded a solution combining speed, accuracy, and global scalability.

Solution: Klarna developed an AI Assistant using LangGraph for controllable agent architecture and LangSmith for testing and monitoring. The system routes requests to specialized agents handling different tasks, with rigorous testing ensuring reliability.

Results (9 months):

• 2.5 million conversations handled to date

• 80% reduction in average customer query resolution time

• Performs work equivalent to 700 full-time staff

• Sebastian Siemiatkowski, CEO: "LangChain has been a great partner in helping us realize our vision for an AI-powered assistant, scaling support and delivering superior customer experiences across the globe"

(LangChain Blog - Klarna Case Study, February 2025)

LinkedIn: SQL Bot for Employee Data Access

Challenge: LinkedIn needed to democratize data access across the organization, enabling employees from all functions to independently query data without SQL knowledge or data team bottlenecks.

Solution: LinkedIn's SQL Bot is a multi-agent system built on LangChain and LangGraph. It transforms natural language questions into SQL by finding appropriate tables, writing queries, fixing errors, and enforcing permissions.

Impact:

• Employees across functions can independently access data insights

• Natural language interface removes technical barriers

• Automated error correction improves reliability

• Hierarchical agent system ensures proper data governance

(LangChain Blog, January 2025)

Uber: Automated Code Migration

Challenge: Uber's Developer Platform team needed to perform large-scale code migrations across their massive codebase, a labor-intensive and error-prone process when done manually.

Solution: Uber built a network of specialized agents using LangGraph to automate unit test generation. Each agent handles a specific step in the migration workflow, operating independently while sharing context through LangGraph's state management.

Results:

• Automated unit test generation for large-scale refactors

• Reduced time for code migration projects

• Improved accuracy by eliminating manual errors

• Dedicated Developer Platform AI team formed to expand agentic tools

(LangChain Blog, January 2025)

Elastic: Security Threat Detection

Challenge: Elastic needed to orchestrate AI agents for real-time threat detection scenarios in their security operations platform, handling high-volume data streams and complex decision-making.

Solution: Elastic migrated from basic LangChain chains to LangGraph as they added more features. Their multi-agent system coordinates specialized agents for different threat detection tasks, with LangGraph managing complex state and workflows.

Impact:

• Real-time threat detection and response

• Reduced labor-intensive SecOps tasks

• Improved response time to security incidents

• GenAI features integrated into core security platform

(LangChain Blog, January 2025; February 2025)

AppFolio: Property Management Copilot

Challenge: AppFolio needed to build Realm-X, an AI-powered copilot helping property managers make faster decisions across residents, vendors, units, bills, and work orders.

Solution: After testing higher-level abstractions, AppFolio switched to LangGraph for controllable agent architecture. The platform provides a conversational interface for querying information, getting help, and executing bulk actions.

Results:

• 2x increase in response accuracy after switching to LangGraph

• 10+ hours saved per week for property managers

• Handles queries, messaging, and action scheduling in one interface

• Production-ready with reliable performance

(LangChain Blog, January 2025)

How to Build with LangChain: Step-by-Step Guide

This section walks through building a simple question-answering application that can retrieve information from documents.

Step 1: Installation and Setup

pip install langchain langchain-openai langchain-community
pip install chromadb tiktoken

Set your OpenAI API key as an environment variable:

export OPENAI_API_KEY="your-api-key-here"

Step 2: Load and Process Documents

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load document
loader = TextLoader("document.txt")
documents = loader.load()

# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = text_splitter.split_documents(documents)

Step 3: Create Embeddings and Vector Store

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

# Create embeddings
embeddings = OpenAIEmbeddings()

# Create and populate vector store
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings
)

Step 4: Set Up Retriever

# Create retriever from vector store
retriever = vectorstore.as_retriever(
    search_kwargs={"k": 3}  # Retrieve top 3 most relevant chunks
)

Step 5: Build the RAG Chain

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA

# Initialize LLM
llm = ChatOpenAI(model="gpt-4", temperature=0)

# Define prompt template
template = """Use the following pieces of context to answer the question.
If you don't know the answer, say that you don't know.

Context: {context}

Question: {question}

Answer:"""

prompt = PromptTemplate(
    template=template,
    input_variables=["context", "question"]
)

# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True,
    chain_type_kwargs={"prompt": prompt}
)

Step 6: Query the System

query = "What are the main findings in the document?"
result = qa_chain({"query": query})

print(result["result"])
print("\nSources:")
for doc in result["source_documents"]:
    print(f"- {doc.metadata}")

This basic example demonstrates the core pattern: load documents, chunk them, embed them, store in a vector database, retrieve relevant chunks for a query, and generate an answer using an LLM with that context.

Retrieval-Augmented Generation (RAG) Explained

Retrieval-Augmented Generation is the most important technique for building practical LLM applications. It solves the fundamental problem of knowledge limitation.

The Core Concept

RAG combines two capabilities:

1. Retrieval: Finding relevant information from an external knowledge source

2. Generation: Using an LLM to generate a response based on that retrieved information

Instead of relying only on the LLM's trained knowledge (which is frozen at a cutoff date), RAG applications dynamically fetch current, domain-specific, or private information and inject it into the prompt (Python LangChain Documentation - RAG Concepts, 2025).

Why RAG Matters

Up-to-date information: Access data published after the model's training cutoff.

Domain-specific expertise: Provide context from specialized knowledge bases the model wasn't trained on.

Reduced hallucination: Ground responses in retrieved facts rather than relying on the model's potentially incorrect memories.

Cost-effective knowledge integration: Cheaper than fine-tuning and easier to update.

40% of LangChain users integrate with vector databases like Pinecone and ChromaDB to power long-term memory in agents (InfoServices, April 2025).

The RAG Pipeline

A typical RAG workflow involves two phases:

Indexing (Offline):

1. Load: Ingest data from sources (PDFs, websites, databases, APIs)

2. Split: Break documents into chunks (typically 500-1500 tokens)

3. Embed: Convert chunks into vector representations using embedding models

4. Store: Index vectors in a vector database for fast similarity search

Retrieval and Generation (Online):

1. Query encoding: Convert user question into a vector using the same embedding model

2. Similarity search: Find the K most relevant document chunks

3. Prompt construction: Combine the query and retrieved context into a structured prompt

4. Generation: LLM generates an answer based on the provided context

(Towards AI - Prasad Mahamulkar, November 2024)

Advanced RAG Techniques

Parent Document Retriever: Embed small chunks for accurate retrieval but provide larger parent documents to the LLM for more context. This balances specificity with comprehensiveness (Towards AI, November 2024).

Query rewriting: Let the LLM reformulate the user's question into a more effective search query before retrieval.

Hybrid search: Combine semantic search (vector similarity) with keyword search (BM25) for better results.

Re-ranking: Use a second model to re-score retrieved documents and select the most relevant ones.

Iterative retrieval: Allow agents to perform multiple retrieval steps, refining queries based on initial results.

LangChain provides abstractions for all these patterns, making advanced RAG techniques accessible without requiring deep expertise in information retrieval (LangChain Blog - RAG Tutorial Part 2, 2025).

Agents and Multi-Agent Systems

Agents represent the evolution from simple chains to autonomous, reasoning systems.

What Are Agents?

An agent is an LLM-powered system that can:

1. Reason: Analyze a goal and decide what steps to take

2. Act: Execute actions using tools (APIs, databases, code interpreters)

3. Observe: See the results of actions

4. Iterate: Adjust the plan based on observations and continue until the goal is achieved

Unlike chains (which follow a predetermined sequence), agents make dynamic decisions at runtime based on the current state (InfoServices, April 2025).

The ReAct Pattern

LangChain's agent framework typically uses the ReAct (Reasoning + Acting) pattern:

1. Thought: The agent reasons about what to do next

2. Action: The agent chooses and executes a tool

3. Observation: The agent sees the tool's result

4. Repeat: This cycle continues until the agent determines it has enough information to answer

This gives LLMs the ability to break down complex problems, gather information incrementally, and course-correct when initial approaches fail (LangChain Blog - Is LangGraph Used in Production, February 2025).

Multi-Agent Systems

As applications grow in complexity, single-agent architectures often hit limits. Multi-agent systems coordinate specialized agents, each handling specific subtasks:

Research assistants: One agent plans the research strategy, another searches for information, a third synthesizes findings, and a fourth critiques the quality.

Coding assistants: A planner breaks down the request, a code generator writes implementation, a tester validates correctness, and a reviewer suggests improvements.

Customer support: A classifier routes tickets to specialized agents (billing, technical support, account management), each with domain-specific knowledge and tools.

LangChain's AgentExecutor runs single agents, while MultiAgentExecutor coordinates agent fleets. Enterprises deploying LangChain in customer support workflows report 35-45% increases in resolution rates using multi-agent designs compared to single-agent systems (InfoServices, April 2025).

LangGraph for Production Agents

Multi-agent systems require sophisticated orchestration: state management, inter-agent communication, human-in-the-loop approval, and error handling. LangGraph was specifically designed for these requirements.

By representing agent workflows as graphs, developers gain:

• Explicit control flow: Define exactly when agents execute and how they hand off work

• Persistent state: Maintain context across long-running tasks that span hours or days

• Checkpointing: Save progress and resume from any point if execution is interrupted

• Streaming: Send real-time updates as agents work

• Human oversight: Pause execution for approval before sensitive actions

Production agents at LinkedIn, Uber, Klarna, and Elastic all use LangGraph for these capabilities (LangChain Blog, September 2025).

Pricing and Cost Structure

LangChain's pricing model has multiple components:

Open-Source Framework: Free

The core LangChain and LangGraph libraries remain free under MIT license. Developers can use them indefinitely without cost. This includes all abstractions, integrations, chains, and local agent execution (GitHub, 2025).

LangSmith Pricing

LangSmith uses a tiered model combining seat licenses with usage-based billing:

Developer Plan (Free):

• 1 seat

• 5,000 base traces per month included

• Additional traces: $0.50 per 1,000 base traces (14-day retention) or $5.00 per 1,000 extended traces (400-day retention)

• Ideal for solo developers and small projects

Plus Plan ($39/seat/month):

• Up to 10 seats

• 10,000 base traces per month included per organization

• Same pay-as-you-go pricing for additional traces

• Team collaboration features

• Designed for growing teams with moderate usage

Enterprise Plan (Custom Pricing):

• Unlimited seats

• Custom trace allotments

• Advanced security, compliance, and deployment options

• Dedicated support

• SSO, SAML, role-based access control

Startup Program:

• Discounted rates for seed-stage startups

• Generous free trace allotments

• 1-year eligibility before graduating to Plus

(LangChain Pricing Page, 2025; MetaCTO - LangSmith Cost Guide, June 2025)

LangGraph Platform Pricing

Developer Plan (Free):

• Self-hosted deployment

• All core features

• 1 million nodes executed included

• Community support

Plus Plan:

• Requires LangSmith Plus subscription ($39/seat/month)

• Usage-based pricing:

  • $0.001 per node executed

  • $0.0007 per minute for dev environment standby

  • $0.0036 per minute for production environment standby

• 1 free development deployment with unlimited node executions

• Managed cloud deployment

• LangGraph Studio for visualization

• Cron scheduling

Enterprise Plan (Custom):

• All Plus features

• Flexible deployment: cloud, hybrid, or self-hosted

• Custom SLAs and support

• Advanced security and compliance

(LangChain Pricing - LangGraph Platform, 2025; ZenML Blog, 2025)

Total Cost Example

A team of 5 developers building a production agent:

• LangSmith Plus: 5 seats × $39 = $195/month

• LangGraph Platform: 1 million node executions × $0.001 = $1,000/month

• Standby time: 30 days × 24 hours × 60 min × $0.0036 = $155/month

Total: ~$1,350/month (excluding LLM API costs from OpenAI, Anthropic, etc.)

For comparison, the open-source route with self-hosted infrastructure costs $0 for LangChain/LangGraph but requires engineering time for observability, deployment, and monitoring infrastructure.

Pros and Cons

Advantages

Rapid prototyping: Pre-built components and abstractions let developers build working prototypes in hours instead of weeks.

Provider flexibility: Unified interface across 25+ LLM providers prevents vendor lock-in and enables cost optimization.

Active ecosystem: 4,000+ contributors, 130 million downloads, and continuous feature additions mean the framework evolves quickly (Contrary Research, February 2025).

Production-ready tooling: LangSmith and LangGraph Platform provide observability, evaluation, and deployment infrastructure often missing from DIY solutions.

Extensive documentation: Comprehensive guides, tutorials, and community resources lower the learning curve.

RAG capabilities: Built-in support for document loading, chunking, embedding, and retrieval makes implementing RAG straightforward.

Multi-agent orchestration: LangGraph enables sophisticated agent coordination patterns that would be extremely difficult to implement from scratch.

Disadvantages

Abstraction overhead: For simple use cases, LangChain's abstractions can feel like unnecessary complexity. Direct API calls might be simpler.

Learning curve: While individual components are simple, understanding how to compose them effectively requires time and experience.

Performance concerns: The abstraction layers add latency. High-performance applications may benefit from more direct implementations.

Breaking changes: As a rapidly evolving framework, LangChain occasionally introduces breaking changes between versions, requiring migration effort.

Documentation overload: The sheer volume of documentation, components, and patterns can be overwhelming for newcomers.

Not ideal for real-time data: LangChain's request-response pattern works poorly for streaming media, telemetry, or high-volume sensor data (Akka Blog, August 2025).

Debugging complexity: Multi-layer abstractions can make debugging difficult when things go wrong, especially without LangSmith.

Enterprise limitations: Out-of-the-box LangChain lacks guarantees for durability and consistency that dedicated workflow engines provide for mission-critical applications (Akka Blog, August 2025).

LangChain vs Alternatives

LangChain vs Direct Provider SDKs (OpenAI, Anthropic)

OpenAI and Anthropic SDKs now include features like function calling, structured outputs, and chat history management—capabilities that originally required LangChain.

Use provider SDKs when: You're building simple applications with a single provider, need maximum performance, or want minimal dependencies.

Use LangChain when: You need multi-provider support, complex RAG pipelines, agent orchestration, or production observability (Neurlcreators - Is LangChain Still Worth Using, June 2025).

LangChain vs LlamaIndex

LlamaIndex (formerly GPT-Index) specializes in data indexing and retrieval. It excels at connecting LLMs to structured data sources like SQL databases, APIs, and knowledge graphs.

Choose LlamaIndex for: Applications primarily focused on retrieval and query optimization, especially over structured data.

Choose LangChain for: Broader application patterns including agents, multi-step workflows, tool integration, and memory management (Xenoss - LangChain vs LlamaIndex, August 2025).

LangChain vs AutoGen

AutoGen (Microsoft) takes a conversation-first approach to multi-agent systems. Agents communicate via message passing, and human feedback integrates naturally.

Choose AutoGen for: Applications where agent collaboration through conversational patterns is central.

Choose LangChain/LangGraph for: Greater control over orchestration, production deployment infrastructure, and integration with the broader LangChain ecosystem (Medium - Aman Raghuvanshi, May 2025).

LangChain vs CrewAI

CrewAI uses a role-based approach where agents have defined roles (researcher, writer, critic) and collaborate on structured tasks.

Choose CrewAI for: Team-oriented agent workflows with clear role assignments and task delegation.

Choose LangChain/LangGraph for: More flexible orchestration patterns, production observability, and proven enterprise adoption (Medium - Aman Raghuvanshi, May 2025).

Common Myths and Misconceptions

Myth: "LangChain is obsolete now that providers have function calling"

Reality: Provider features reduced the need for some simple patterns, but LangChain remains essential for complex applications. RAG pipelines, multi-agent orchestration, memory management, and production observability still require framework-level support. 60% of AI developers working on autonomous agents use LangChain as their primary orchestration layer (InfoServices, April 2025).

Myth: "LangChain is only for Python developers"

Reality: LangChain has full TypeScript/JavaScript support with feature parity to Python. The JavaScript package has seen massive adoption, with a 300% increase in npm downloads from Q1 2024 to Q1 2025 (InfoServices, April 2025).

Myth: "You must use all of LangChain or none of it"

Reality: LangChain is modular. Developers commonly use just the components they need—perhaps only the document loaders and vector store integrations—while handling prompting and model calls directly.

Myth: "LangChain applications are slow"

Reality: The abstraction layers add minimal latency (typically milliseconds). The dominant latency in LLM applications comes from the model inference itself (seconds), not the orchestration framework. LangChain's LCEL supports async execution and streaming, often making applications faster than naive implementations.

Myth: "LangSmith is required to use LangChain"

Reality: LangSmith is optional. The open-source LangChain and LangGraph libraries work perfectly without it. LangSmith adds observability and evaluation capabilities that become valuable as applications grow in complexity.

Myth: "RAG eliminates hallucination"

Reality: RAG significantly reduces hallucination by grounding responses in retrieved facts, but it doesn't eliminate it completely. LLMs can still misinterpret context, ignore provided information, or generate plausible-sounding but incorrect statements. Proper evaluation and quality control remain essential.

Future Outlook and Trends

Agentic AI Becomes Mainstream

The shift from simple chatbots to autonomous agents represents a fundamental change in AI application development. By 2025, agents that can reason, plan, use tools, and execute multi-step workflows are becoming the norm rather than the exception. LangChain and LangGraph are positioned as the infrastructure enabling this transition (Medium - Aman Raghuvanshi, May 2025).

Enterprise Adoption Accelerates

As companies move from AI prototypes to production systems, the need for reliability, observability, and governance increases. LangChain's enterprise features—deployment flexibility, monitoring, evaluation, and security—address these requirements. The $125 million funding round in October 2025 at a $1.25 billion valuation reflects investor confidence in this enterprise opportunity (Bitcoin Ethereum News, October 2025).

LangChain 1.0 and Platform Maturation

LangChain announced version 1.0 in October 2025, marking the framework's maturation from experimental tool to production-grade platform. This release includes middleware for enhanced context management and a production-ready runtime. The focus shifts from adding features to ensuring stability and backward compatibility (Bitcoin Ethereum News, October 2025).

Integration with Model Context Protocol

Anthropic's Model Context Protocol (MCP) standardizes how LLMs connect to external data sources and tools. LangChain's roadmap includes MCP integration, ensuring compatibility with this emerging standard while maintaining support for existing patterns (Neurlcreators, June 2025).

Democratization of AI Development

LangChain lowers the barrier to entry for AI application development. As the framework matures, more developers without specialized ML expertise will build sophisticated AI systems. LangChain Academy offers free courses teaching developers to build with the framework, accelerating this democratization (GitHub - LangChain AI, 2025).

Hybrid and Self-Hosted Deployments

Regulatory requirements, data residency concerns, and latency considerations drive demand for flexible deployment options. LangGraph Platform's hybrid model (control plane in LangChain's cloud, data plane on-premises) and self-hosted option address these needs, making LangChain viable for regulated industries like healthcare and finance (LangChain Pricing, 2025).

FAQ

1. Is LangChain free to use?

Yes. The core LangChain and LangGraph libraries are open-source under MIT license and free for any use. LangSmith (observability platform) and LangGraph Platform (managed deployment) offer free tiers for individual developers and paid plans for teams and enterprises.

2. What programming languages does LangChain support?

LangChain has full support for Python and JavaScript/TypeScript. Both implementations have feature parity and active maintenance.

3. Which LLM providers work with LangChain?

LangChain integrates with 25+ providers including OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral, Cohere, Hugging Face models, and many others. The unified interface allows switching providers without code changes.

4. Do I need to use LangGraph with LangChain?

No. LangGraph is optional and designed for specific use cases: cyclic workflows, stateful agents, human-in-the-loop patterns, and complex orchestration. Simple chains work fine with just LangChain.

5. Can LangChain be used for production applications?

Yes. Companies like Klarna, LinkedIn, Uber, and Elastic run production systems on LangChain and LangGraph serving millions of users. LangSmith provides production observability, and LangGraph Platform offers managed deployment infrastructure.

6. How does LangChain pricing work?

The open-source framework is free. LangSmith charges per seat ($39/month for Plus plan) plus usage-based fees for traces. LangGraph Platform charges per node execution ($0.001/node) plus standby time. A free tier exists for individual developers.

7. What's the difference between LangChain and ChatGPT?

ChatGPT is an application—a chatbot interface to OpenAI's GPT models. LangChain is a framework for building your own AI applications. You could use LangChain to build something like ChatGPT, but with custom features like RAG, specialized tools, or multi-agent workflows.

8. Does LangChain work offline?

LangChain can work with locally hosted models (Ollama, Hugging Face transformers) for offline LLM inference. However, many integrations (OpenAI, Anthropic, cloud vector databases) require internet connectivity.

9. How steep is the LangChain learning curve?

Basic use cases (simple chains, RAG) can be learned in a few days. Advanced patterns (multi-agent systems, custom tools, production deployment) require weeks to master. The extensive documentation and community resources help accelerate learning.

10. Can I use LangChain with my own data and keep it private?

Yes. LangChain can work with self-hosted vector databases (ChromaDB, FAISS) and local models, keeping all data on your infrastructure. For cloud deployments, LangSmith never uses customer data for training and offers self-hosted options for sensitive use cases.

11. What vector databases does LangChain support?

LangChain integrates with 20+ vector databases including FAISS, Chroma, Pinecone, Weaviate, Milvus, Qdrant, pgvector (Postgres), and Elasticsearch. The abstraction layer allows swapping databases without application changes.

12. Is LangChain suitable for mobile apps?

LangChain's Python and JavaScript libraries can power backend APIs that mobile apps call. The framework isn't designed for on-device execution due to LLM resource requirements, but LangGraph Platform's API deployment makes mobile integration straightforward.

13. How does LangChain handle errors in agent execution?

LangChain agents include retry logic for failed tool calls, error handling callbacks, and maximum iteration limits to prevent infinite loops. LangGraph adds checkpointing, allowing agents to resume from save points after failures.

14. Can LangChain applications scale to handle high traffic?

Yes, with proper architecture. The open-source framework itself scales horizontally—run multiple instances behind a load balancer. LangGraph Platform includes horizontal scaling with task queues designed for high-throughput production deployments.

15. What's the community like around LangChain?

Very active. Over 4,000 open-source contributors, thousands of community-built templates and integrations, active Discord and GitHub Discussions, and regular community events. The LangChain team is responsive to issues and feature requests.

16. How often does LangChain release updates?

Weekly. The team maintains a rapid release cadence with new features, integrations, and bug fixes shipping regularly. Version 1.0 (October 2025) marks a shift toward stability while maintaining active development.

17. Does LangChain support streaming responses?

Yes. Both LangChain and LangGraph support streaming tokens as they're generated, providing real-time feedback to users. This is essential for maintaining engagement during long agent executions.

18. Can I migrate from LangChain to another framework later?

Migration difficulty depends on how deeply integrated your code is. Applications using mainly LangChain's model interfaces and basic chains can migrate relatively easily. Complex multi-agent LangGraph applications would require significant refactoring.

19. What's the best way to learn LangChain?

Start with the official tutorials (RAG application, Q&A with chat history). Build a small project applying LangChain to a problem you understand. Explore the template repository for examples. Take LangChain Academy courses (free). Join the Discord community for help.

20. Is LangChain appropriate for regulated industries?

Yes, with proper deployment. Self-hosted LangChain with local models and vector databases can meet data residency requirements. LangGraph Platform's hybrid and self-hosted options address compliance needs. Many healthcare, financial, and government organizations use LangChain.

Key Takeaways

• LangChain is the leading framework for building LLM applications, with 117,560+ GitHub stars, 130 million downloads, and unicorn status after raising $125 million at a $1.25 billion valuation in October 2025.

  
  

• It solves critical LLM limitations by adding memory, external data access via RAG, tool integration, multi-provider support, and agent capabilities—turning isolated language models into production-ready applications.

  
  

• The ecosystem includes four products: LangChain (open-source framework), LangGraph (agent orchestration), LangSmith (observability), and LangGraph Platform (deployment infrastructure).

  
  

• Major companies rely on LangChain in production: Klarna handles 2.5 million conversations, LinkedIn's SQL Bot democratizes data access, Uber automates code migrations, and Elastic orchestrates security threat detection.

  
  

• RAG is the most important pattern, used by 40% of LangChain users to ground LLM responses in current, domain-specific, or private information retrieved from external sources.

  
  

• Multi-agent systems coordinate specialized agents for complex workflows, delivering 35-45% higher resolution rates in customer support compared to single-agent systems.

  
  

• Pricing is accessible: Open-source framework is free, LangSmith starts at $0 (Developer plan) or $39/seat/month (Plus plan), and LangGraph Platform uses pay-as-you-go pricing at $0.001 per node execution.

  
  

• The framework is modular and flexible: Use only the components you need, swap LLM providers without code changes, and choose from self-hosted to fully managed deployments.

  
  

• LangGraph enables production agents with stateful execution, human-in-the-loop patterns, checkpointing, and sophisticated orchestration—capabilities that make autonomous agents reliable at scale.

  
  

• The future is agentic: LangChain is positioned at the center of the shift from simple chatbots to autonomous, reasoning systems that can plan, use tools, and execute multi-step workflows.

Actionable Next Steps

1. Install LangChain and build your first simple chain—a prompt template connected to an LLM. This hands-on experience will clarify the core concepts.

   
   

2. Follow the official RAG tutorial to implement document question-answering. RAG is the foundation for most practical LLM applications and worth mastering early.

   
   

3. Sign up for LangSmith's free Developer plan and add tracing to your application. Visibility into execution flow is invaluable for debugging and optimization.

   
   

4. Explore the LangChain templates repository on GitHub. Find a template similar to your use case and study how it's implemented.

   
   

5. Take a LangChain Academy course (free) to learn structured patterns for building agents, implementing RAG, and deploying applications.

   
   

6. Join the LangChain Discord community to ask questions, get help debugging, and stay updated on new features and best practices.

   
   

7. Build a small project applying LangChain to a real problem in your domain. Hands-on application is the fastest way to understand the framework's strengths and limitations.

   
   

8. If building multi-agent systems, study LangGraph examples and case studies from companies like LinkedIn and Uber to understand production patterns.

   
   

9. Evaluate cost implications for your expected usage. Calculate LLM API costs (OpenAI, Anthropic) plus LangSmith/LangGraph Platform fees based on projected traffic.

   
   

10. Contribute back to the open-source community by sharing templates, writing documentation, or reporting issues. Active participation accelerates learning and builds reputation.

Glossary

1. Agent: An LLM-powered system that can reason about goals, decide which tools to use, execute actions, observe results, and iterate until achieving the goal.

   
   

2. Chain: A sequence of operations linked together, where the output of one step becomes the input to the next. Chains orchestrate LLMs, retrievers, and custom logic.

   
   

3. Embedding: A numerical vector representation of text that captures semantic meaning, enabling similarity comparisons and search.

   
   

4. LangChain Expression Language (LCEL): A declarative syntax for composing chains that supports streaming, async execution, and automatic parallelization.

   
   

5. LangGraph: A library for building stateful, multi-agent applications represented as directed graphs, with support for cycles, checkpointing, and human-in-the-loop patterns.

   
   

6. LangSmith: An observability and evaluation platform providing tracing, debugging, prompt management, and cost tracking for LLM applications.

   
   

7. Large Language Model (LLM): A neural network trained on massive text datasets to understand and generate human language (e.g., GPT-4, Claude, Gemini).

   
   

8. Memory: Components that store conversation history, tool outputs, or user preferences, enabling applications to maintain context across interactions.

   
   

9. Multi-Agent System: An architecture where multiple specialized AI agents collaborate on tasks by dividing work, sharing context, and coordinating execution.

   
   

10. Prompt Template: A reusable text template with variable placeholders that structures input to LLMs, supporting dynamic content and few-shot examples.

    
    

11. RAG (Retrieval-Augmented Generation): A technique that combines information retrieval from external sources with LLM generation, grounding responses in retrieved facts.

    
    

12. ReAct Pattern: A reasoning approach where agents alternate between Reasoning (thinking about what to do) and Acting (executing tools), iterating until reaching a goal.

    
    

13. Retriever: A component that searches a knowledge base (typically a vector store) and returns relevant documents based on a query.

    
    

14. Tool: An executable capability that agents can invoke—API calls, database queries, calculations, web searches, or custom functions.

    
    

15. Vector Store: A specialized database that indexes embeddings and supports fast similarity search to find semantically related content.

References

1. LangChain Documentation (2025). Introduction to LangChain. Retrieved from https://python.langchain.com/docs/introduction/

   
   

2. Wikipedia (August 25, 2025). LangChain. Retrieved from https://en.wikipedia.org/wiki/LangChain

   
   

3. Bitcoin Ethereum News (October 22, 2025). LangChain Celebrates Three Years with New Funding and Strategic Developments. Retrieved from https://bitcoinethereumnews.com/tech/langchain-celebrates-three-years-with-new-funding-and-strategic-developments/

   
   

4. Contrary Research (February 2025). Report: LangChain Business Breakdown & Founding Story. Retrieved from https://research.contrary.com/company/langchain

   
   

5. LangChain Blog (February 27, 2025). How Klarna's AI assistant redefined customer support at scale for 85 million active users. Retrieved from https://blog.langchain.com/customers-klarna/

   
   

6. LangChain Blog (January 30, 2025). Top 5 LangGraph Agents in Production 2024. Retrieved from https://blog.langchain.com/top-5-langgraph-agents-in-production-2024/

   
   

7. LangChain Blog (September 4, 2025). Building LangGraph: Designing an Agent Runtime from first principles. Retrieved from https://blog.langchain.com/building-langgraph/

   
   

8. LangChain Blog (February 6, 2025). Is LangGraph Used In Production? Retrieved from https://blog.langchain.com/is-langgraph-used-in-production/

   
   

9. InfoServices (April 30, 2025). LangChain & Multi-Agent AI in 2025: Framework, Tools & Use Cases. Retrieved from https://blogs.infoservices.com/artificial-intelligence/langchain-multi-agent-ai-framework-2025/

   
   

10. Medium - Aman Raghuvanshi (May 26, 2025). Agentic AI #3 — Top AI Agent Frameworks in 2025: LangChain, AutoGen, CrewAI & Beyond. Retrieved from https://medium.com/@iamanraghuvanshi/agentic-ai-3-top-ai-agent-frameworks-in-2025-langchain-autogen-crewai-beyond-2fc3388e7dec

    
    

11. Medium - Vinod Rane (August 18, 2025). LangChain vs LangGraph: Choosing the Right Framework for Your AI Workflows in 2025. Retrieved from https://medium.com/@vinodkrane/langchain-vs-langgraph-choosing-the-right-framework-for-your-ai-workflows-in-2025-5aeab94833ce

    
    

12. Xenoss (August 26, 2025). LangChain vs LangGraph vs LlamaIndex (2025): Best LLM framework for multi-agent systems. Retrieved from https://xenoss.io/blog/langchain-langgraph-llamaindex-llm-frameworks

    
    

13. DEV Community (September 1, 2025). The Complete Beginner's Guide to LangChain: Why Every Developer Needs This Framework in 2025(PART 1). Retrieved from https://dev.to/fonyuygita/the-complete-beginners-guide-to-langchain-why-every-developer-needs-this-framework-in-2025part-1-2d55

    
    

14. Neurlcreators (June 17, 2025). Is LangChain Still Worth Using in 2025? Retrieved from https://neurlcreators.substack.com/p/is-langchain-still-worth-using-in

    
    

15. Akka Blog (August 8, 2025). 25 LangChain Alternatives You MUST Consider In 2025. Retrieved from https://akka.io/blog/langchain-alternatives

    
    

16. Latenode Blog (2025). LangChain Framework 2025: Complete Features Guide + Real-World Use Cases for Developers. Retrieved from https://latenode.com/blog/langchain-framework-2025-complete-features-guide-real-world-use-cases-for-developers

    
    

17. Medium - Amit Patriwala (May 21, 2025). LangChain: A Comprehensive Framework for Building LLM Applications (With code). Retrieved from https://medium.com/@patriwala/langchain-a-comprehensive-framework-for-building-llm-applications-e2800dba2753

    
    

18. GitHub - langchain-ai (October 20, 2025). LangChain Organization. Retrieved from https://github.com/langchain-ai

    
    

19. Frederick AI (2025). Founder Story: Harrison Chase of LangChain. Retrieved from https://www.frederick.ai/blog/harrison-chase-langchain

    
    

20. Latent Space Podcast (September 6, 2023). The Point of LangChain — with Harrison Chase of LangChain. Retrieved from https://www.latent.space/p/langchain

    
    

21. TED AI San Francisco (2025). Harrison Chase. Retrieved from https://tedai-sanfrancisco.ted.com/speakers/harrison-chase/

    
    

22. Towards AI - Prasad Mahamulkar (November 1, 2024). Retrieval-Augmented Generation (RAG) using LangChain, LlamaIndex, and OpenAI. Retrieved from https://pub.towardsai.net/introduction-to-retrieval-augmented-generation-rag-using-langchain-and-lamaindex-bd0047628e2a

    
    

23. LangChain Documentation (2025). Retrieval augmented generation (RAG). Retrieved from https://python.langchain.com/docs/concepts/rag/

    
    

24. LangChain Documentation (2025). Build a Retrieval Augmented Generation (RAG) App: Part 1. Retrieved from https://python.langchain.com/docs/tutorials/rag/

    
    

25. LangChain Pricing (2025). Plans and Pricing. Retrieved from https://www.langchain.com/pricing

    
    

26. ZenML Blog (2025). LangGraph Pricing Guide: How Much Does It Cost? Retrieved from https://www.zenml.io/blog/langgraph-pricing

    
    

27. MetaCTO (June 24, 2025). The True Cost of LangSmith - A Comprehensive Pricing & Integration Guide. Retrieved from https://www.metacto.com/blogs/the-true-cost-of-langsmith-a-comprehensive-pricing-integration-guide

    
    

28. MetaCTO (July 12, 2025). LangGraph Pricing Explained - Integration & Maintenance Costs. Retrieved from https://www.metacto.com/blogs/langgraph-pricing-explained-a-deep-dive-into-integration-maintenance-costs

    
    

29. LangSmith AWS Marketplace (2025). LangSmith and LangGraph Platform. Retrieved from https://aws.amazon.com/marketplace/pp/prodview-vmzygmggk4gms

    
    

30. Vstorm Glossary (August 1, 2025). What is LangChain founder? Retrieved from https://vstorm.co/glossary/langchain-founder/

    
    

31. Vstorm Glossary (August 4, 2025). What is LangChain history? Retrieved from https://vstorm.co/glossary/langchain-history/