LangChain is an open-source framework built to help developers and enterprises create applications powered by large language models (LLMs) — quickly, efficiently, and at scale.
It doesn't matter if you're building a chatbot, a document search tool, or a more complex AI agent; LangChain provides the tools to take your project from idea to production in a fraction of the time.
When Did Langchain Launch?
Harrison Chase launched LangChain in October 2022, and it quickly gained popularity, becoming the fastest-growing open-source project on GitHub. Following its enormous popularity, generative AI (genAI) has been more accessible to hobbyists and startups, thanks in large part to LangChain. This also coincided with the historic introduction of OpenAI's ChatGPT the following month.
Recognising its rapid growth and potential, co-founder Harrison Chase officially incorporated LangChain in 2023 and brought on Ankush Gola as co-founder.
In April 2023, LangChain secured a $10 million seed round led by Benchmark. The funding gave the founders the resources to scale their vision, enabling more developers to build, deploy, and optimise intelligent applications using language models.
How Langchain Works
LangChain supports the full AI engineering lifecycle, from prototyping to production. With this platform, developers can monitor, test, and refine LLM applications more efficiently, ensuring faster and more reliable deployment. With over 600 integrations, it offers one of the most extensive libraries for connecting LLMs to data sources, APIs, and external tools.
To speed up the development cycle, LangChain offers a library of reference architectures in the form of templates. These templates let developers easily implement solutions for a variety of use scenarios, such as GPT-based research assistants and RAG chatbots.
In addition to these high-level templates, LangChain provides customised prompt templates to guarantee that user input is structured for language models in the best possible way.
The customisation of LLM-driven applications may be made even faster and easier with the help of these prompt templates, which can include few-shot samples, compose several questions, or partially format prompts. Used by more than 100,000 companies, LangChain has become a go-to solution for teams looking to develop flexible, future-proof AI systems.
How to Build and Deploy LLM Apps with LangChain
LangChain simplifies the entire lifecycle of building, testing, and deploying large language model (LLM) applications:
1. Development
An organisation can start building with LangChain’s open-source framework and flexible integrations. If you're creating simple chains or advanced agents, tools like LangGraph let you model stateful workflows with support for streaming responses and human-in-the-loop feedback. This is perfect for building AI applications. It also means LangGraph is a stateful, orchestration framework that brings added control to agent workflows.
2. Productionisation
Once the app is built, organisations can use LangSmith, which brings it to life in production. LangSmith helps you debug, monitor, and evaluate your LLM workflows, making it easier to spot issues, improve performance, and ensure your app behaves as expected before full-scale deployment.
3. Deployment
When you're ready to go live, the LangGraph platform allows organisations to turn their LangGraph-based applications into scalable APIs or AI Assistants, complete with deployment infrastructure, versioning, and operational tools designed for real-time usage.
It's important to note that LangChain is designed to support the full lifecycle of building LLM-powered applications, and tools like LangGraph and LangSmith extend its capabilities significantly. LangChain implements a standard interface for large language models and related technologies, such as embedding models and vector stores, and integrates with hundreds of providers.
What Does LangChain Offer?
LangChain is used to build AI applications that go beyond basic text generation. If you want to build a smart assistant, summarise reports, or connect your LLM to external tools, LangChain gives you the building blocks.
Here are some practical ways people are using it:
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Chatbots
Chatbots are one of the most popular use cases for LLMs, and LangChain makes them smarter. You can feed your chatbot the right context, connect it to APIs, and plug it into platforms your team already uses.
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Summarisation
LangChain can help you condense long documents, academic papers, meeting transcripts, or even your inbox into something more digestible. Just prompt your LLM and let it do the heavy lifting.
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Question answering
LangChain helps your LLM pull accurate answers from specific sources, like a company knowledge base, scientific papers, or structured databases. It works well with integrations like Wolfram Alpha, arXiv, or PubMed.
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Data Augmentation
LangChain-powered LLMs can generate synthetic examples that match your real data, perfect for boosting machine learning models or testing new ideas.
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Virtual Agents
LangChain’s Agent modules allow you to build agents that can make decisions and take actions. Connected with tools like RPA (Robotic Process Automation), these agents can handle repetitive tasks and workflows on their own.
How to get started with LangChain
LangChain is an open-source framework designed to make building with large language models (LLMs) easier, faster, and more powerful. It’s completely free to use, and you can explore the source code anytime on GitHub.
If you're working in Python, getting started is simple. Just run: pip install langchain. To install all LangChain dependencies (rather than only those you find necessary), you can run the command pip install langchain[all].
AI Market Growth
So, is it worth investing in this? Absolutely! If you are still unsure, the stats you will see will reveal that getting on board this train will help your organisation in the long run. The global application development software market reached an estimated value of $255 billion in 2024 and is on track to grow at an annual growth rate (CAGR) of 20.9 per cent, potentially reaching $1.7 trillion by 2034.
Within this broader space, LangChain operates in a fast-growing niche: AI application development tools. This segment was valued at $4.8 billion in 2023 and is forecast to expand at a CAGR of over 23 per cent, surpassing $30 billion by 2032.
As of the end of 2023, North America led the AI code tools market, accounting for approximately 35 per cent of the global share. This reflects both regional AI maturity and the concentration of enterprise-level adoption.
The market is further accelerated by breakthroughs in AI capabilities. The launch of OpenAI’s o1 reasoning model in September 2024 and the surge in agent-based AI solutions have propelled growth in the sector.
According to Gartner, by 2028, 33 per cent of enterprise applications are expected to embed agentic AI — a significant jump from less than 1 per cent in 2024. This shift is expected to enable autonomous decision-making for up to 15 per cent of routine business tasks.
LangChain is strategically positioned to serve this booming segment by providing the infrastructure and developer tooling that AI agent startups need to scale and bring products to market faster.
LangChain’s SaaS Model
While LangChain’s business strategy is built around a hybrid model: offering its LangChain framework as a free, open-source tool, while monetising through its complementary platforms LangSmith and LangGraph, via tiered SaaS pricing.
LangSmith uses a flexible pricing structure designed to serve developers at every stage. Individual developers and hobbyists can start with the free Developer plan. This includes 5,000 traces per month and a pay-as-you-go option for additional usage.
Startups can access custom discounted pricing, including a monthly distribution of free traces, making it easier to prototype without heavy upfront costs.
For teams needing more features, the Plus plan includes 10,000 traces, higher rate limits, support for up to 10 users, and dedicated email support. This plan is ideal for growing teams looking to scale while maintaining flexibility.
Larger organisations can opt for a custom Enterprise plan, which includes everything in the Plus plan and adds SSO, SLAs, self-hosting options, custom rate limits, and more. Enterprise clients also receive personalised onboarding, architectural reviews, access to a shared Slack channel, and a dedicated customer success manager.
Across all plans (excluding the free Developer tier), LangSmith provides access to essential tools for debugging, prompt management, human-in-the-loop labelling, evaluation, and bulk data export, enabling teams to build, monitor, and refine LLM-powered applications with confidence.
What’s next for Langchain?
As AI rapidly evolves, the challenge is no longer just about accessing powerful models – it’s about using them effectively. LangChain bridges that gap by offering a modular, open-source framework that lets developers create LLMs in practical, production-ready ways.
The good thing about it is that LangChain provides the infrastructure to turn ideas into fully functioning applications.
Its tight integrations with tools like vector databases, APIs, retrieval systems, and orchestration frameworks (like LangGraph and LangSmith) make it a go-to solution for anyone looking to build contextual, responsive, and scalable AI systems.
With a large community support, growing enterprise adoption, and ongoing development, LangChain is more than a library. It’s fast becoming the backbone for a new wave of AI software.
So if you're a solo developer experimenting on the weekend or part of a team rolling out AI in production, LangChain gives you the tools to start fast, iterate confidently, and scale without compromise. The LangChain website features a list of guides and tutorials that can be beneficial if you would like to learn more.
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