LangChain Alternatives (2026): 10 Tools Compared

LangChain is one of the best-known frameworks for building apps on top of AI models. Its own repository calls it an agent engineering platform, and it's open source under the MIT licence. In plain words, a framework like this is a box of ready-made parts that a developer uses to connect a model to documents, tools and workflows, so they don't write every connection from scratch. Some developers find it more than they need, or simply prefer a different style.

These are ten LangChain alternatives in no particular order, sorted alphabetically. Some are code libraries, one is a visual builder, and a few suit web developers who work in TypeScript. Descriptions come from each project's own page, so check the current details there. If you're a business owner rather than a developer, my guide to what AI agents are and how your business can use them is the place to start.

LangChain alternatives at a glance

In the free plan column, Yes means the project's own page describes free, open-source software. Trial only means a free trial without an ongoing free plan. No means neither is stated. Some projects also have separate hosted services, which this table doesn't cover.

Tool	Type	Good for	Free plan
CrewAI	Agent framework	Teams of role-playing agents in Python	Yes
DSPy	Programming framework	Programming language models instead of hand-writing prompts	Yes
Griptape	Agent framework	Modular Python agents with tools and memory	Yes
Haystack	Orchestration framework	Search and question answering over your documents	Yes
Langflow	Visual builder	Building agents and workflows on a canvas	Yes
LlamaIndex	Data framework	Connecting language models to your documents	Yes
Mastra	TypeScript framework	AI apps and agents in TypeScript	Yes
Pydantic AI	Python framework	Agents with checked, typed outputs	Yes
txtai	Search and workflow framework	Semantic search and language model workflows	Yes
Vercel AI SDK	TypeScript toolkit	Adding AI features to web apps	Yes

Ten frameworks for building apps on top of AI models

CrewAI

Best for: Python developers who like to describe an AI team as roles, such as researcher and writer.

DSPy

Best for: Developers who would rather program and tune model behaviour than keep rewriting prompt text by hand.

Griptape

Best for: Python developers who want a modular kit of agents, tools and memory.

Haystack

Best for: Teams building question answering and search over their own documents with clear building blocks.

Langflow

Best for: People who prefer to build agents and workflows visually rather than by writing everything as code.

LlamaIndex

Best for: Developers whose main job is connecting a language model to documents, files and other data.

Mastra

Best for: Web developers who work in TypeScript and want agents and workflows in the same language.

Pydantic AI

Best for: Python developers who like their AI answers checked against a defined shape before use.

txtai

Best for: Developers who want meaning-based search and simple language model workflows in one package.

Vercel AI SDK

Best for: Web developers adding chat and AI features to a website or app built in TypeScript.

What an LLM app framework does for you

A language model on its own just answers a prompt. To build something useful, such as a support helper that reads your policies, you need to feed it the right documents, give it tools, remember a conversation and check the result. A framework supplies those parts so a developer can focus on your problem.

You may never use one yourself. But if you hire a developer or an agency, knowing the vocabulary helps you ask better questions. Terms like agent, retrieval, workflow and memory all describe parts of the same kit.

Code-first or visual: which style of LangChain alternative fits

Some options are libraries that developers write in Python or TypeScript. Others give you a canvas where you drag blocks and connect them. Code gives more control and is easier to test and keep in version control. Visual tools are faster for first drafts and easier to explain to non-developers.

• Python code. The most common choice, with the widest set of examples.
• TypeScript code. A natural fit if your website or app is already built that way.
• Visual canvas. Good for prototypes and for explaining a workflow to colleagues.
• Document-focused kits. Good when the main job is searching your own files.

Questions to ask before you switch away from LangChain

• How much of my existing code depends on LangChain-specific parts?
• Does the new project cover the tools, models and data sources I use?
• Is the project active, and does it have recent releases and clear docs?
• What licence does it use, and does that suit commercial work?
• Will my team find examples and help when something breaks?
• Can I move step by step, or do I need to rewrite everything at once?

If you want AI working in your business and don't want to learn all this, an AI consultant in the UK can help, and my guide to AI agents and automation explains the wider picture.

One more thing to weigh is how you'll find help. A framework with lots of examples, active discussions and clear docs saves hours when something breaks. Skim the project page and its recent activity, and see whether other people are asking and answering questions about the problems you expect to meet.

Reviewing code and keeping keys safe in any AI framework

Tutorials and sample projects are handy, but review code before you run it, especially anything that installs packages, reads files or calls other services. An agent that can run tools can also do damage, so give it only the access it needs.

Never paste secrets or API keys into prompts, notebooks or shared files. Keep them in environment settings your tool provides, and use a separate key for each project so you can switch one off quickly.

Why a smaller AI framework can be a sensible pick

Smaller projects are often simpler to read, and you can understand what they do in an afternoon. They can also change quickly or go quiet. At least one popular visual builder I came across while researching has been archived by its owner, so look at the project's recent activity and the date of its last release before you build on it.

Do you make an AI app framework or agent toolkit? Tool makers can ask to be considered for a featured place on this list.

A short checklist before you move from LangChain

Write down what you built and which parts you rely on. Choose two options from this page that cover them, rebuild one small piece, and compare how readable it feels. Keep the one your team will be comfortable maintaining, and note a date to review it.

Frequently asked questions

What can I use instead of LangChain?

You can use another Python framework, a TypeScript toolkit, a document-focused library or a visual builder. The right one depends on your language, your data and how much control you want.

Is LangChain open source?

Yes. Its repository lists the MIT licence. Always read the licence of any project you plan to use in client or commercial work.

Do I need a framework at all?

Not always. For a simple feature, calling a model directly can be enough. Frameworks help once you add documents, tools, memory and several steps.

Are these frameworks free to use?

The projects listed here describe themselves as free, open-source software. Some makers also run separate hosted services, so check each project's page for the details.

Can a non-developer use these?

Mostly they're built for developers, though visual builders lower the bar. If code isn't your thing, a developer or consultant can set one up for you.