LangChain vs STORM
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
Open source · MIT
Open-source LLM system that researches a topic online and drafts a Wikipedia-style article with citations.
The short answer
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Choose STORM if you need…
- Developers who want to build a customizable research or report-writing pipeline
- Writers who want a first-draft outline and source list before writing
- Researchers exploring a new topic who want a structured overview with references
- Teams experimenting with different language models and search engines
Side-by-side
| LangChain | STORM | |
|---|---|---|
| Type | Framework | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 149k | 31.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 13 months ago |
What each one is
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
STORM
STORM is a research system built on large language models. Given a topic, it searches the internet, simulates conversations to gather information, builds an outline, and writes a full-length article with citations. Co-STORM extends it so a human can take part in the discussion and steer the research. The README says the output is not publication-ready and often needs significant editing, but experienced Wikipedia editors have found it useful for pre-writing.
Best for
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
STORM
- Developers who want to build a customizable research or report-writing pipeline
- Writers who want a first-draft outline and source list before writing
- Researchers exploring a new topic who want a structured overview with references
- Teams experimenting with different language models and search engines
Strengths
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
STORM
- Modular design: language models and search engines can be swapped, per the README's litellm and retriever support
- Produces outlines and cited text rather than a single unsourced answer
- Open source under the MIT license with an active research and release history
Not ideal for
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
STORM
- Anyone needing publication-ready text, since the README says articles often need significant edits
- People without an API key for a language model or search provider, since the system depends on them
- Users who need a finished no-setup app, since the README centers on a Python package and code setup
Trade-offs
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
STORM
- The README says output is not publication-ready and often needs significant editing
- Requires configured language model and search API keys, which may carry costs
- Installation and customization assume Python and some comfort with code
Getting started
STORM
Install the library from PyPI
pip install knowledge-stormClone the repository for source install
git clone https://github.com/stanford-oval/storm.gitFrequently asked questions
What is the difference between LangChain and STORM?
LangChain: A Python framework for building AI agents and apps powered by language models. STORM: Open-source LLM system that researches a topic online and drafts a Wikipedia-style article with citations. LangChain suits python developers building llm-powered applications, while STORM suits developers who want to build a customizable research or report-writing pipeline.
Are LangChain and STORM free?
LangChain: Free and open source. STORM: Free and open source.
Is LangChain or STORM more popular?
LangChain has more GitHub stars (149k) than STORM (31.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangChain and STORM?
Other options people compare them with include LlamaIndex, Semantic Kernel, CrewAI, OpenUI, VoltAgent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.