claude-quickstarts vs LangChain
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
Starter projects that show how to build apps with the Claude API, from support agents to browser and desktop a
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
The short answer
Choose claude-quickstarts if you need…
- Developers who want to see working Claude API examples before writing their own app
- Teams evaluating which Claude capabilities fit a use case such as support, data analysis, or browser automation
- Developers already using Slack, Linear, or Sentry who want an example integration to study
- Students learning how AI agents are wired together with tools and sessions
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
Side-by-side
| claude-quickstarts | LangChain | |
|---|---|---|
| Type | Learning resource | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 19.1k | 149k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | TypeScript | Python |
| Last code update | 6 days ago | 4 days ago |
What each one is
claude-quickstarts
Claude Quickstarts is a collection of example projects from Anthropic meant to help developers begin building applications on the Claude API. Each project is a starting point you can run, read, and then adapt to your own needs. The collection covers a range of examples, including a customer support agent, a financial data analyst, computer and browser use demos, and several Managed Agents integrations with tools like Slack, Linear, and Sentry.
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.
Best for
claude-quickstarts
- Developers who want to see working Claude API examples before writing their own app
- Teams evaluating which Claude capabilities fit a use case such as support, data analysis, or browser automation
- Developers already using Slack, Linear, or Sentry who want an example integration to study
- Students learning how AI agents are wired together with tools and sessions
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
Strengths
claude-quickstarts
- Maintained by Anthropic and published under the MIT license, so the code can be reused and modified
- Covers many different use cases, from chat apps to scheduled agents and browser or desktop control
- Each quickstart is in its own folder, so you can work on one example without setting up the others
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
Not ideal for
claude-quickstarts
- Non-developers who want a ready-made app without writing or configuring code, since the README assumes API setup
- Anyone needing production-ready code, because several projects say they are reference or teaching examples rather than production code
- Users without a Claude API key, since the README says one is needed to use these quickstarts
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
Trade-offs
claude-quickstarts
- Projects vary a lot in complexity and maturity; some are explicitly minimal examples and not production code
- The README is truncated and does not give full setup steps for each project, so you will need to read each subfolder's instructions
- Several projects depend on specific Claude features, such as Managed Agents or newer toolset versions, which may change over time
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
Getting started
claude-quickstarts
Clone the repository
git clone https://github.com/anthropics/claude-quickstartsFrequently asked questions
What is the difference between claude-quickstarts and LangChain?
claude-quickstarts: Starter projects that show how to build apps with the Claude API, from support agents to browser and desktop a. LangChain: A Python framework for building AI agents and apps powered by language models. claude-quickstarts suits developers who want to see working claude api examples before writing their own app, while LangChain suits python developers building llm-powered applications.
Are claude-quickstarts and LangChain free?
claude-quickstarts: Free and open source. LangChain: Free and open source.
Is claude-quickstarts or LangChain more popular?
LangChain has more GitHub stars (149k) than claude-quickstarts (19.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to claude-quickstarts and LangChain?
Other options people compare them with include LlamaIndex, OpenAI Cookbook, Semantic Kernel, CrewAI, OpenUI.
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.