LangChain has been a popular choice for developers working with Large Language Models (LLMs).

As the AI landscape rapidly evolves, many developers are seeking LangChain alternatives that better meet their specific needs — whether for data processing, integration with certain platforms, or advanced model customization.

Perhaps you're already drowning in abstractions, struggling with inconsistent APIs, or simply craving something more optimized. If so, you're not alone.

In this article, we'll look at 15 alternatives to LangChain that might save your sanity (and your codebase). Whether you're looking for something more lightweight, more specialized, or just different, we'll help you out!

15 LangChain alternatives

We looked at a range of tools and frameworks that could serve as LangChain alternatives, each offering unique capabilities and approaches to AI development:

  • Low-code/no-code platforms: n8n, Flowise and Langflow;
  • Data integration frameworks: LlamaIndex, txtai and Haystack;
  • AI agent frameworks: CrewAI, SuperAGI, Autogen, Langdroid and Rivet;
  • Specialized LLM tools: Semantic Kernel, Transformers Agent, Outlines and Claude Engineer.
15 LangChain alternatives
15 LangChain alternatives

We selected these 15 frameworks based on the awesome-langchain repo, which curates a list of LangChain-related projects.

Here’s a short list of LangChain alternatives for 2024:

Tool Description Relationship to LangChain Development Approach Language Deployment Focus Key Features
n8n Low-code platform
combining AI and automation		 Independent framework
with LangChain integration		 Low-code / no-code
with code customization		 JavaScript with
Python support		 Flexible (cloud
and self-hosted)		 AI integration, workflow automation,
LangChain orchestration, RAG, API integration
Flowise Open-source low-code platform
for LLM applications		 LangChain wrapper / extension Low-code /
no-code platform		 JavaScript Local development
and production-ready		 LLM integration, workflow builder,
multi-agent systems, RAG
Langflow An open-source visual IDE
for AI pipelines and agents		 LangChain wrapper / extension Low-code /
no-code platform		 Python-based Production-ready,
cloud-native		 LLM integration, multi-agent systems,
RAG, workflow builder, prompt engineering
LlamaIndex Data framework
for LLM applications		 Independent framework Code-first Python / TypeScript Production-ready LLM integration, RAG,
vector store integration, data analysis
txtai All-in-one embeddings
database for AI workflows		 Independent framework Code-first Python-based Local development
and production-ready		 LLM integration, RAG, Semantic search,
workflow builders, Vector store integration
Haystack End-to-end framework for
LLM applications and search		 Independent framework Code-first Python-based Production-ready LLM integration, RAG, semantic search,
API integration, multi-model support
CrewAI Framework for orchestrating
role-playing, autonomous AI agents		 Independent framework
with LangChain integration		 Code-first Python-based Production-ready Multi-agent systems, workflow/pipeline builders,
task management and delegation, flexible process execution,
LLM integration, memory and caching capabilities
SuperAGI Framework for autonomous
AI agent development		 Independent framework Code-first with
GUI elements		 Python-based Production-ready
(cloud and local)		 Multi-agent systems, tool/plugin ecosystems,
workflow builders, memory management
AutoGen Framework for building
cooperative AI agents		 Independent framework Code-first Python-based Production-ready Multi-agent systems, LLM integration,
tool use support, workflow builders
Langroid Lightweight framework for
LLM-powered applications		 Independent framework Code-first Python-based Production-ready Multi-agent systems, workflow builders,
memory management, tool/plugin ecosystems
Rivet Visual AI programming
environment and library		 Independent framework / tool Low-code / no-code
with code integration		 TypeScript-based Local development
and production-ready		 LLM integration, workflow builders,
prompt engineering, API integration
Semantic Kernel SDK integrating LLMs
with conventional programming		 Independent framework Code-first Multi-language
(C#, Python, Java)		 Production-ready LLM integration, multi-agent systems,
workflow builders, memory management, plugin ecosystems
Transformers Agent A flexible framework
for AI agents using LLMs		 Independent framework Code-first Python-based Local development
and prototypes		 LLM integration, multi-agent systems,
tool/plugin ecosystems
Outlines Library for structured
LLM output generation		 Independent framework Code-first Python-based Production-ready LLM integration, prompt engineering,
structured output generation, multi-model support
Claude Engineer CLI for AI-assisted
software development		 Independent framework / tool Code-first
with CLI		 Python-based Local development Code generation, API integration,
tool/plugin ecosystem

The features we've chosen to highlight include:

  • Relationship to LangChain: whether the tool is an extension of LangChain or an independent framework;
  • Development approach: main method of building applications (e.g. low-code, code-first);
  • Language: the primary programming language or environment the tool supports;
  • User interface: the main interface for interacting with the tool (e.g. GUI, API, CLI);
  • Deployment focus: the tool's primary deployment target (e.g. local, production, cloud);
  • Key features: a list of the most important capabilities and functionalities.

It's worth noting that many tools span multiple categories or offer features that overlap with other groupings. The table aims to capture these nuances and provide a detailed picture of each tool's strengths and focus areas, helping developers choose the most suitable option for their specific needs.

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Most LangChain alternatives in this article are completely open-sourced or have free tiers. However, users need to provide their own API keys for LLM access and take care about hosting. This may involve extra costs depending on usage. From now on we will only mention whether a tool itself is free or has paid tiers.

Low-code and no-code platforms

n8n

n8n – a powerful source-available low-code platform that combines AI capabilities with traditional workflow automation. This approach allows users with varying levels of expertise to build custom AI applications and integrate them into business workflows.

As one of the leading LangChain alternatives, n8n offers an intuitive drag-and-drop interface for building AI-powered tools like chatbots and automated processes. It strikes a balance between ease of use and functionality, allowing for low-code development while enabling advanced customization when needed.

n8n as an alternative to LangChain
n8n as an alternative to LangChain

Key Features:

  1. LangChain integration: utilize LangChain powerful modules with n8n user-friendly environment and additional features;
  2. Flexible deployment: choose between cloud-hosted or self-hosted solutions to meet security and compliance requirements;
  3. Advanced AI components: implement chatbots, personalized assistants, document summarization, and more using pre-built AI nodes;
  4. Custom code support: add custom JavaScript / Python code when needed;
  5. LangChain vector store compatibility: integrate with various vector databases for efficient storage and retrieval of embeddings;
  6. Memory management: implement context-aware AI applications with built-in memory options for ongoing conversations;
  7. RAG (Retrieval-Augmented Generation) support: enhance AI responses with relevant information from custom data sources;
  8. Scalable architecture: handles enterprise-level workloads with a robust, scalable infrastructure.
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One of the distinguishing features of n8n is its extensive library of pre-built connectors. This vast ecosystem of integrations allows users to incorporate AI capabilities into existing business processes - from customer support to data analytics and beyond.

Pricing:

  • Free Community edition for self-hosting
  • Cloud version starts at 35$/month
  • Custom pricing for enterprise clients

Flowise

Flowise is an open-source, low-code platform for creating customized LLM applications. It offers a drag-and-drop user interface and integrates with popular frameworks like LangChain and LlamaIndex.

However, users should keep in mind that while Flowise simplifies many aspects of AI development, it can still prove difficult to master for those unfamiliar with the concepts of LangChain or LLM applications in general.

In addition, developers may have to resort to the code-first approaches offered by other LangChain platforms for highly specialized or performance-critical applications.

Flowise – an open-source, low-code platform for creating customized LLM applications
Flowise – an open-source, low-code platform for creating customized LLM applications

Key features:

  1. Integration with popular AI frameworks such as LangChain and LlamaIndex;
  2. Support for multi-agent systems and RAG;
  3. Extensive library of pre-built nodes and integrations;
  4. Tools to analyze and troubleshoot chatflows and agentflows (these are two types of apps you can build with Flowise).

Pricing:

  • Flowise is free and open-source for self-hosting
  • Cloud version starts at 35$/month

Langflow

Langflow is an open-source visual framework for building multi-agent and RAG applications. Langflow smoothly integrates with the LangChain ecosystem, generating Python and LangChain code for production deployment. This feature bridges the gap between visual development and code-based implementation, giving developers the best of both worlds.

Langflow also excels in its provision of LangChain tools and components. These pre-built elements allow developers to quickly add functionality to their AI applications without having to code from scratch.

Langflow – an open-source visual framework for building multi-agent and RAG applications
Langflow – an open-source visual framework for building multi-agent and RAG applications

Key features:

  1. Drag-and-drop interface for building AI workflows;
  2. Integration with various LLMs, APIs and data sources;
  3. Python and LangChain code generation for deployment.

Pricing:

  • Langflow offers a free-to-use model, available as both a self-hosted project and as a cloud-based service.
  • Although the cloud version of Langflow is free, its default vector store is backed by AstraDB. This cloud database has usage-based pricing.

Data integration and retrieval frameworks

LlamaIndex

LlamaIndex is a powerful data framework designed for building LLM applications. It provides a set of tools for data ingestion, indexing and querying, making it an excellent choice for developers looking to create context-augmented AI applications.

LlamaIndex as a LangChain alternative
LlamaIndex as a LangChain alternative

Key features:

  1. Extensive data connectors for various sources and formats;
  2. Advanced vector store capabilities with support for 40+ vector stores;
  3. Powerful querying interface, including RAG implementations;
  4. Flexible indexing capabilities for various use cases.

Pricing: LlamaIndex is open-source and free to use

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Is LlamaIndex better than LangChain?
One of LlamaIndex's strengths is its extensive support for vector stores, surpassing many competitors in terms of integration capabilities. This makes it particularly useful for projects requiring sophisticated vector search capabilities.

txtai

txtai is an all-in-one embedding database that offers a comprehensive solution for semantic search, LLM orchestration and language model workflows. It combines vector indexes, graph networks and relational databases to enable advanced features like vector search with SQL, topic modeling and RAG. txtai is able to function independently or as a knowledge source for LLM prompts.

The flexibility of txtai is enhanced by its support for Python and YAML-based configurations, making it accessible to developers with different preferences and skill levels. The framework also offers API bindings for JavaScript, Java, Rust and Go, extending its use across different tech stacks.

txtai – an all-in-one embeddings database for semantic search, LLM orchestration and workflows
txtai – an all-in-one embeddings database for semantic search, LLM orchestration and workflows

Key features:

  1. Vector search with SQL integration;
  2. Multimodal indexing for text, audio, images and video;
  3. Language model pipelines for various NLP tasks;
  4. Workflow orchestration for complex AI processes.

Pricing: Free and open-source

Haystack

Haystack is a versatile open-source framework for building production-ready LLM applications, including chatbots, intelligent search solutions and RAG LangChain alternatives. Its extensive documentation, tutorials and active community support make it an attractive option for both junior and experienced developers in the LLM space.

Haystack – an open-source framework for building production-ready LLM applications
Haystack – an open-source framework for building production-ready LLM applications

Key features:

  1. Modular architecture with customizable components and pipelines;
  2. Support for multiple model providers (e.g., Hugging Face, OpenAI, Cohere);
  3. Integration with various document stores and vector databases;
  4. Advanced retrieval techniques such as Hypothetical Document Embeddings (HyDE), which can significantly improve the quality of retrieved context for LLM prompts.

Pricing: Free and open-source

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Is Haystack better than LangChain?
Compared to other frameworks, Haystack offers a more comprehensive set of ready-made tools for building end-to-end AI applications. Opt for Haystack if your focus is on building powerful search and retrieval systems or if you need advanced NLP capabilities for querying large datasets.

AI agent and automation frameworks

CrewAI

CrewAI is a framework for orchestrating role-playing, autonomous AI agents.

CrewAI stands out for its ability to create a "crew" of AI agents, each of which has specific roles, goals and backstories. For instance, you can have a researcher agent gathering information, a writer agent crafting content and an editor agent refining the final output – all working in concert within the same framework.

CrewAI – a framework for orchestrating role-playing, autonomous AI agents
CrewAI – a framework for orchestrating role-playing, autonomous AI agents

Key features:

  1. Multi-agent orchestration with defined roles and goals;
  2. Flexible task management with sequential and hierarchical processes;
  3. Integration with various LLMs and third-party tools;
  4. Advanced memory and caching capabilities for context-aware interactions.

Pricing: Free and open-source

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In other words, CrewAI could be a superior alternative to LangChain when it comes to a more nuanced and flexible problem-solving process compared to single-agent systems.

SuperAGI

SuperAGI is a powerful open-source LangChain framework alternative for building, managing and running autonomous AI agents at scale. Unlike frameworks that focus solely on local development or building simple chatbots, SuperAGI provides a comprehensive set of tools and features for creating production-ready AI agents.

One of SuperAGI's strengths is its extensive toolkit system, reminiscent of LangChain's tools, but with a more production-oriented approach. These toolkits allow agents to interact with external systems and third-party services, making it easy to create agents that can perform complex real-world tasks.

SuperAGI – an open-source LangChain alternative for building, managing and running autonomous AI agents
SuperAGI – an open-source LangChain alternative for building, managing and running autonomous AI agents

Key features:

  1. Autonomous agent provisioning: easily build and deploy scalable AI agents;
  2. Extensible toolkit system: enhance agent capabilities with various integrations that are similar to LangChain tools;
  3. Performance telemetry: monitor and optimize agent performance in real-time;
  4. Multi-vector DB support: connect to different vector databases to improve agent knowledge.

Pricing: Free and open-source

Autogen

AutoGen is a Microsoft framework focused on building and orchestrating AI agents to solve complex tasks.

Comparing Autogen to LangChain, it's important to note that while both frameworks aim to simplify the development of LLM-powered applications, they have different approaches and strengths.

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LangChain focuses on chaining together different components for language model applications, while AutoGen emphasizes multi-agent interactions and conversations. Autogen is more advantageous if you need autonomous AI agents capable of independently executing tasks and generating content with minimal intervention.
AutoGen – a Microsoft framework focused on building and orchestrating AI agents
AutoGen – a Microsoft framework focused on building and orchestrating AI agents 


Key features:

  1. Multi-agent conversation framework;
  2. Customizable and conversable agents;
  3. Enhanced LLM inference with caching and error handling;
  4. Diverse conversation patterns for complex workflows.

Pricing: Free and open-source

Langroid

Langroid is an intuitive, lightweight and extensible Python framework for building LLM-powered applications. It offers a fresh approach to LLM app development, focusing on simplifying the developer experience. Langroid utilizes a Multi-Agent paradigm inspired by the Actor Framework, allowing developers to set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign tasks and have them collaboratively solve problems through message exchange.

While Langroid offers a fresh take on LLM app development, it's important to note that it doesn't use LangChain, which may require some adjustment for developers. This independence, however, allows Langroid to implement its own optimized approaches to common LLM application challenges.

Langroid – a lightweight and extensible Python framework for building LLM-powered applications
Langroid – a lightweight and extensible Python framework for building LLM-powered applications

Key features:

  1. Multi-agent paradigm: inspired by the Actor framework, enables collaborative problem-solving;
  2. Intuitive API: simplified developer experience for quick setup and deployment;
  3. Extensibility: easy integration of custom components and tools;
  4. Production-ready: designed for scalable and efficient real-world applications.

Pricing: Free and open-source

Rivet

Rivet stands out among promising LangChain alternatives for production environments by offering a unique combination of visual programming and code integration. This open-source tool provides a desktop application for creating complex AI agents and prompt chains.

While tools like Flowise and Langflow focus primarily on visual development, Rivet bridges the gap between visual programming and code integration: visual approach to AI agent creation can significantly speed up development, whereas Rivet TypeScript library allows visually created graphs to be executed in existing applications.

Rivet – an open-source desktop application for creating complex AI agents and prompt chains
Rivet – an open-source desktop application for creating complex AI agents and prompt chains

Key features:

  1. Unique combination of a node-based visual editor for AI agent development with a TypeScript library for real-time execution;
  2. Support for multiple LLM providers (OpenAI, Anthropic, AssemblyAI);
  3. Live and remote debugging capabilities that allow developers to monitor and troubleshoot AI agents in real time, even when deployed on remote servers.

Pricing: Free and open-source

Specialized LLM tools

Semantic Kernel

Semantic Kernel is a LangChain alternative developed by Microsoft and designed to integrate LLMs into applications. It stands out for its multi-language support, offering implementations in C#, Python and Java. This makes Semantic Kernel attractive to a wider range of developers, especially those working on existing enterprise systems written in C# or Java.

Another key strength of Semantic Kernel is its built-in planning capabilities. While LangChain offers similar functionality through its agents and chains, Semantic Kernel planners are designed to work with its plugin system, allowing for more complex and dynamic task orchestration.

Semantic Kernel is a LangChain alternative developed by Microsoft for integrating LLMs into applications
Semantic Kernel is a LangChain alternative developed by Microsoft for integrating LLMs into applications

Key features:

  1. Plugin system for extending AI capabilities;
  2. Built-in planners for complex task orchestration;
  3. Flexible memory and embedding support;
  4. Enterprise-ready with security and observability features.

Pricing: Free and open-source

Hugging Face Transformers Agent

Hugging Face Transformers library has introduced an experimental agent system for building AI-powered applications. Transformers agents offer a promising alternative for developers, especially those already familiar with the Hugging Face ecosystem. However, its experimental nature and complexity may make it less suitable for junior devs or rapid prototyping compared to more established frameworks like LangChain.

Hugging Face Transformer Agents – an experimental library for building AI-powered applications
Hugging Face Transformer Agents – an experimental library for building AI-powered applications

Key Features:

  1. Support for both open-source (HfAgent) and proprietary (OpenAiAgent) models;
  2. Extensive default toolbox that includes document question answering, image question answering, speech-to-text, text-to-speech, translation and more;
  3. Customizable tools: users can create and add custom tools to extend the agent's capabilities;
  4. Smooth integration with Hugging Face vast collection of models and datasets.

Pricing: Free and open-source

Outlines

Outlines is a framework focused on generating structured text. While LangChain provides a comprehensive set of tools for building LLM applications, Outlines aims to make LLM outputs more predictable and structured, following JSON schemas or Pydantic models. This can be particularly useful in scenarios where precise control over the format of the generated text is required.

Outlines – a framework focused on generating structured text outputs
Outlines – a framework focused on generating structured text outputs

Key features:

  1. Multiple model integrations (OpenAI, transformers, llama.cpp, exllama2, Mamba);
  2. Powerful prompting primitives based on Jinja templating engine;
  3. Structured generation (multiple choices, type constraints, regex, JSON, grammar-based);
  4. Fast and efficient generation with caching and batch inference capabilities.

Pricing: Free and open-source

Claude Engineer

Claude Engineer is a LangChain Anthropic alternative that brings the capabilities of Claude-3/3.5 models directly to your command line. This tool provides a smooth experience for developers who prefer to work in a terminal environment. While it does not offer the visual workflow building capabilities of low-code platforms like n8n or Flowise, the Claude Engineer command-line interface is suitable for  developers who prefer a more direct, code-centric approach to AI-assisted development.

Claude Engineer – a LangChain Anthropic alternative that brings the capabilities to your command line
Claude Engineer – a LangChain Anthropic alternative that brings the capabilities to your command line

Key features:

  1. Interactive chat interface with Claude 3 and Claude 3.5 models;
  2. Extendable set of tools, including file system operations, web search capabilities and even image analytics;
  3. Execution of Python code in isolated virtual environments;
  4. Advanced auto-mode for autonomous task completion.

Pricing: Free

n8n as a workaround for LangChain

After we've already reviewed 15 popular LangChain alternatives, let’s take a closer look at what makes n8n stand out.

It offers a unique approach to AI development by smoothly integrating AI capabilities with traditional workflow automation.

With an extensive library of pre-built connectors and native LangChain integration, n8n offers the flexibility to utilize LangChain powerful modules in a more user-friendly environment. Even creating custom tools is possible with minimal programming.

The HTTP Request tool and Custom n8n workflow tool nodes are the easiest ways to extend LangChain tools. Unlike many other code-first frameworks, n8n users can create custom tools while enjoying the drag-and-drop UI with minimal coding.

Here are some common tasks and how to solve them with n8n + LangChain:

LangChain workflows for data extraction

Data extraction is a common task in LLM-powered apps. It helps language models to improve accuracy by working with the specific document. If the document is too large, parts of the document are used in the LLM context. RAG is one approach to that.

In n8n, you can work with an in-memory vector store or connect to multiple external vector databases. Finally, you can even pass SQL query results back to the AI agent, completely bypassing vector storage. Here are just a few examples:

Take a look at an n8n tutorial on how to work with Zep – one of the vector store providers available both in the cloud and as a self-hosted installation.

LangChain workflows for LLM agents

Many LangChain alternatives focus on autonomous agents. If you wonder what these are, check out our clear yet detailed article about AI agents.

In n8n, you can build such agents via LangChain nodes. Alternatively, you can build your custom agent using smaller building blocks. The whole process can fit into a single workflow, or you can dedicate each agent to a separate workflow.

Here’s a recording of the introductory session on AI agents and automation:

LangChain custom tools

There are three ways you can customize LangChain in n8n:

  1. Connect a Custom n8n workflow tool node. This is a simple yet powerful way to connect your existing business automations with LangChain. In most cases, you can do without coding.
  2. Use the HTTP request tool node. This tool allows for querying external services through an API in just a single node. This tool can also present HTML pages in a condensed way, which is great for extracting data from the page.
  3. Finally, you can write custom code in the LangChain Code and Code tool nodes. This is the most advanced approach and requires a good knowledge of the LangChain framework.

Here’s a new in-depth walkthrough on how to use the HTTP Request tool with several use cases.

OpenAI assistants in n8n

OpenAI assistants are a proprietary alternative to LangChain agents. While these assistants are vendor-locked within the OpenAI ecosystem, they are very easy to create and support several useful features like document content extraction right out of the box.

n8n adds an extra layer of automation, allowing you to create fully autonomous OpenAI assistants and connect them to your favorite business tools.

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Take a look at our step-by-step tutorial on how to create such OpenAI assistants in our article on the best AI chatbots.

Wrap up

In this guide, we looked at 15 powerful LangChain alternatives for AI development. Each alternative offers unique features and approaches to working with LLM, catering to different developer needs and skill levels.

As you consider these options, give n8n a try.

With its intuitive interface, extensive integration library, and powerful AI capabilities, n8n offers a unique blend of simplicity and flexibility for your AI projects.

Create your own AI-powered workflows

Build complex automations 10x faster, without fighting APIs

What’s next?

To deepen your understanding of AI development and automation: