The 7 best LangChain alternatives in 2026

Large language models (LLMs) are smart, but they aren't that smart. They can't solve dark matter, they can't tell me what my wife wants for dinner (I don't think even she knows, to be fair), and, by default, they can't connect directly to your internal documents, information, and systems. While all of these are of equal importance, I assume you're here for the integrations.

LangChain—and similar developer toolkits—help engineering teams bridge that gap between LLMs and internal information, so they can build integrations, agents, and AI apps that work within their own infrastructure. I did in-depth research and testing, and consulted IRL developers to compile the best LangChain alternatives.

The best LangChain alternatives

• Zapier for no-code AI orchestration

• LlamaIndex for document-centric RAG

• Haystack for Python developers

• Flowise for graph-based orchestration

• Microsoft Semantic Kernel for coding flexibility

• Google Vertex AI for Google Gemini developers

• Amazon Bedrock AgentCore for AWS users

What makes the best LangChain alternative?

Review articles are inherently subjective. What makes a good LangChain competitor to me may not fit your exact criteria. To help mitigate that disconnect, I'll tell you upfront how I graded every tool on my list.

• Functionality and features: While every developer toolkit is a bit different, any LangChain alternative should have advanced features and functionality to help you build AI and automation workflows. Specifically, I was looking for things like retrieval augmented generation (RAG), AI orchestration for multi-step flows, multi-agent capabilities, and context retention. 

• Ease of use: If the tool was easy for me to figure out, that means it's pretty dang accessible. If the interface made as much sense to me as ancient Sumerian, however, I deemed it a bit more challenging to learn. I also awarded bonus points to documentation quality, quickstarts, clean UI/UX, and reliable customer support. If the product is a software development kit (SDK), I made sure teams could access files and support documents easily. 

• Integration and flexibility: These types of tools all need to have robust integration capabilities—otherwise, your AI tool building is confined to a sandbox. The entries all needed to have comprehensive compatibility with a multitude of LLMs, APIs, internal documentation, and app integrations to help developers construct the best end product possible.

• Security and compliance: When you link an LLM to your internal systems, the technology now has access to information that you may not want broadcast on the front page of The New York Times. I prioritized aspects like OpenAI/AWS policies, network controls, and strong data encryption to help safeguard your systems as strongly as possible.

The best LangChain alternatives at a glance

Best LangChain alternative for no-code AI orchestration

Zapier (Web)

Zapier pros:

• Comprehensive workflow builder

• 8,000+ apps and integrations

• AI agents, chatbots, databases, and forms

• AI Copilot to build workflows and agents with natural language

Zapier cons:

• Free plan limited to two-step workflows

LangChain and similar AI tool builders can give teams freedom in how they construct technology; it can also give headaches. From coding to testing to integration, you could spend hundreds of hours in a dark room building something that may or may not end up being useful—or, you can adopt a proven solution.

With Zapier, you can build and launch an AI chatbot or AI agent in minutes, not months—no coding necessary. 

The AI chatbot can help your support team connect with customers, scale support processes, and generate more leads. First, train the chatbot with your own content by uploading FAQs, docs, or private links. Then, customize the look and feel of the bot and connect it with your most important customer-facing channels like your website, social media, Slack, ticketing systems, or any one of 8,000+ available integrations. 

The AI agent is like adding a virtual employee of the month to your internal team. Start by using Zapier Copilot to build an agent with specific instructions—say, if you want it to qualify sales leads automatically or answer customer concerns in Zendesk. From there, you can enrich the AI agent with company documents and added insights, monitor agent activity, and adjust tasks and instructions.

Once you've created your ideal AI assistant, add it to your workflows for an end-to-end productivity powerhouse. You can build a comprehensive flow that automates customer support interactions, creates an iron-clad lead management pipeline, or manages FAQs and requests from Slack.

That may seem like a lot, but it's business as usual for Zapier. Zapier's AI orchestration capabilities ensure no tool, workflow, or team exists in a silo—everything is interconnected and gets executed at precisely the right time to keep your business moving full steam ahead.

Zapier is easy to learn, easy to use, and can get you started with AI agents and chatbots in a fraction of the time as LangChain. Because you're on the Zapier blog right now, I recommend getting a second opinion from the folks who've built AI systems on Zapier—or try out one of these templates to see what's possible.

Zapier pricing: Free plan available, paid plans starting at $13.33/month for Chatbots and $33.33/month for Agents.

Best LangChain alternative for document-centric RAG

LlamaIndex (Web)

LlamaIndex pros:

• Document-focused build flow

• Python and TypeScript compatibility

LlamaIndex cons:

• Credit-metered usage without self-hosting

• Basic customer support outside of the Enterprise plan

When I was about six years old, my parents pulled me out of school on my birthday to take me to see a llama in the local petting zoo. The llama spit in my face. Despite my lifelong beef with an entire species, I was pleasantly surprised with my interactions in LlamaIndex.

The platform prioritizes document management and starts the entire tool-building process from that perspective. Teams first upload internal documents to LlamaCloud—a document processing system that allows you to parse, extract, or index information. From there, devs can use that information in conjunction with LlamaIndex, the AI agent-building toolkit, and Workflows, the multi-step AI workflow builder. It's worth noting, however, that if you'd rather just jump right into coding (skipping the document upload process), you can do that, too.

I could only play around with LlamaCloud, but from what I saw, it has a clear UI/UX and would be easy for any user to work with. Navigation is simple, with everything you need to get started either at your fingertips or clearly displayed on the side of the screen. In fact, the software even has a perennial Resources tab on the right-hand side that populates with relevant tutorials and information that changes depending on which tab you're on—something I found extremely useful.

RAG, memory, state management, human-in-the-loop, reflection, and extensive data security capabilities are all prominently featured in LlamaIndex. It also has Python and TypeScript SDKs that teams can integrate into their dev stack, integrations with popular LLMs, and a wealth of data connections. LlamaIndex also supports vibe coding apps—which I know is like nails on a chalkboard for some seasoned developers, but could be a nice touch for new coders dipping their toes into this type of technology.

LlamaIndex encourages you to start the chatbot and AI tool-building journey with documentation and then expand from there (although you could start directly with code if you're so inclined). If one of your main concerns is linking and organizing internal data, risk the spit and give it a try.

LlamaIndex pricing: Free plan available, Starter ($50/month), Pro ($500/month), Enterprise (contact LlamaIndex)

Best LangChain alternative for Python developers

Haystack (Web)

Haystack pros:

• Deepset Studio visual builder integration

• Active community with tutorials

• Free and open-source

Haystack cons:

• Only compatible with Python

• Code-first UX in Haystack

At first glance, Haystack by Deepset seems like a bare-bones platform that some Harvard undergrad threw together in his dorm room. Sure, it's an open-source AI orchestration tool that helps developers build LLM applications, but it's primarily housed in a text editor. A text editor that only supports Python, mind you.

But zoom out, and you'll be quick to find that Haystack offers much more than meets the eye. The interface is actually pretty useful; it offers compatibility with OpenAI, Anthropic, Amazon Bedrock, Google Gemini, and more. You can build and edit AI agents, multimodal apps, and RAG systems directly on the editor, while adding logic loops and branches to your heart's content.

You can also link your project with model providers, vector databases, and other complementary AI tools, and keep tabs on it with built-in tracing, logging, and evaluation. Not only that, but Haystack also has an active community where devs can view tutorials, access templates, and chat on Discord.

Zoom out even further, and you start to get the picture of a pretty comprehensive tool. In comes Deepset Studio, an LLM orchestration framework built on Haystack that upgrades the experience from undergrad to Ph.D. candidate. With Deepset Studio, teams can design and modify their AI projects in a drag-and-drop visual editor; it allows you to connect integrations, access pre-built templates, test and debug prototypes, and explore variables like inputs, outputs, and prompt builders.

The beauty of Haystack is in the eye of the beholder. If you want a no-frills coding environment to build an AI tool, you got it. If you want a visual builder with expanded features, you've got that, too (by way of Deepset Studio). Just be sure you're comfortable with Python.

Haystack pricing: Free to use, contact Deepset for Haystack Enterprise pricing

Best LangChain alternative for graph-based orchestration

Flowise (Web)

Flowise pros:

• Cloud and self-hosting options

• Workday integrations

Flowise cons:

• Less suited for custom coding options compared to others on the list

• Advanced security features are only available in higher-tiered plans

Flowise is an open-source platform that helps teams build AI agents and workflows in a visual format. It was recently acquired by Workday—and if you read between the lines a bit, you'll conclude that if a multi-billion-dollar company found it worth bringing it to their existing customer base, it must be pretty good.

The UI/UX is clean, and teams can easily access chatflows, agentflows, and executions, and manage documents. The visual builder is simple, and the drag-and-drop style is a nice touch for folks who may not be as confident with coding as seasoned developers. 

As I dove into more of the specifics, I found that teams have a lot of options for building AI systems. The first major point of note: many of Flowise's components are actually dependent on LangChain. Flowise is more like a no-code wrapper that just adds a graphical user interface (GUI) on top of LangChain abstractions. So, you can use the same code-based abstractions of LangChain—like chains, agents, memory modules, and retrievers—as drag-and-drop nodes on a visual canvas. 

In my opinion, this makes the app best suited for small and medium-sized companies. A GUI works for smaller workflows, but as your flows get bigger, they get harder to debug and maintain—which means you'd likely want to transfer to a full-code option instead. That said, Flowise has built a fairly substantial community on spaces like Discord and GitHub, so you can peruse threads and how-to guides if you get stuck along the way.

Flowise is a good choice for teams that want a visual builder integrated with the Workday ecosystem. If you want to expand the integrations further, connect Flowise with Zapier to connect it to your entire tech stack. Zapier connects to thousands of apps, so you can use Flowise with all the software your team uses.

Flowise pricing: Free plan available, Starter ($35/month), Pro ($65/month), Enterprise (contact Flowise)

Best LangChain alternative for coding flexibility

Microsoft Semantic Kernel (Web)

Microsoft Semantic Kernel pros:

• Multi-code language support with C#, Python, and Java

• AI chatbot building and multi-agent workflows

• Compatibility with the Microsoft ecosystem and multiple LLMs

Microsoft Semantic Kernel cons:

• Code-intensive interface

• Teams may be locked into cloud-specific knowledge

I hope you enjoyed our brief interlude on visual AI builders, because now we dive into the technical deep end to round out my list. The first entry in my code-heavy crescendo is Semantic Kernel, Microsoft's open-source SDK that allows developers to build AI agents and applications in either C#, Python, or Java.

Semantic Kernel is suitable for both small and enterprise teams, and specializes in chatbots and multi-agent workflows. Developers can connect to a number of LLMs like OpenAI, Azure OpenAI, Hugging Face, and NVIDIA. From there, you can build an AI agent that leverages tools or user plugins, RAG, and planning capabilities. Teams can also orchestrate multiple agents via complex workflows and intertwined activities.

Since this is an SDK, there's not really a good product screenshot for me to share here. It would be like reviewing an IKEA couch and taking a picture of a cushion or one of the shiny screws that inevitably fall out of the package and get lost in the folds of time. Just know that Semantic Kernel is primarily code-based, so be prepared to spend a lot of time working with loops and functions.

Semantic Kernel has a robust plugin ecosystem that fits nicely in the wider Microsoft world, but teams can also implement their own native code, prompts, or LLM specs to create something truly unique. Security-wise, teams deploy the SDK within their own system, so they can have greater control over what information is exposed.

Overall, Microsoft Semantic Kernel is a true-blue SDK for developing AI chatbots and multi-agent flows. If you're comfortable in a full-coding environment, this could be a good option for your team.

Microsoft Semantic Kernel pricing: Free to use

Best LangChain alternative for Google Gemini developers

Google Vertex AI (Web)

Google Vertex AI pros:

• Compatibility with the Gemini ecosystem and multiple LLMs

• AI agents, AI tools, and model workflow building

• Enterprise data privacy measures

Google Vertex AI cons:

• Usage-based pricing 

• Steep learning curve

I'm the type of person who gravitates toward specialities. If I need new running shoes, I don't go to the general sports store—I go to the running shoe store that measures my exact foot size, arch, heel drop ratio, and other variables I haven't even thought of. If I were a developer who wanted to build AI agents and models on the Gemini infrastructure, I'd use Google's proprietary development platform.

Google Vertex AI is a tool that allows developers to enhance and customize Gemini models. This is massively important because it means teams can use it to natively access Google's search index to verify facts—other frameworks need heavy custom tooling to do this. If you'd like to access another AI model instead, you have your choice of 200+, like Imagen, Veo, and Anthropic.

Vertex AI Studio is where devs can fine-tune prompts and build foundation model workflows and AI applications. You can modify over 200 proprietary, open, or third-party models via the Model Garden. Once you select a model, you can engage in prompt design and tweak text and image responses via adapter tuning, reinforcement learning from human feedback (RLHF), and even change the "temperature" of the response—say, if you want the LLM to sound more like a robot or Ernest Hemingway.

Developers can then use their AI creation in conjunction with proprietary data sources or third-party integrations, all while knowing their data is safe thanks to Google's extensive AI/ML privacy commitment. 

Meanwhile, the Agent Builder is the agent development kit (ADK) where you can build production-ready agents by working directly in Python code. Teams can connect agents, enrich them with proprietary data via Model Context Protocol (MCP), insert them into custom workflows, integrate with APIs, and scale and share them with other team members via Gemini Enterprise (previously Google Agentspace)—a nice touch if you have multiple coworkers or employees who need to access the AI agent.

Overall, Google Vertex AI is the best place to build an AI agent or tool that runs off the Gemini system. If you'd like to get even more out of your build, connect Vertex with Zapier to seamlessly integrate your creation into a comprehensive workflow.

Google Vertex AI pricing: Token-based pricing

Best LangChain alternative for AWS users

Amazon Bedrock AgentCore (Web)

Amazon Bedrock AgentCore pros:

• Compatibility with the AWS ecosystem and multiple LLMs

• AI tool building, training, and deployment

• Sandbox and isolated working environments for security

Amazon Bedrock AgentCore cons:

• Usage-based pricing 

• Steep learning curve

At this point, nearly every company on earth is investing in the AI space, whether they belong there or not. Before long, we're bound to see Ninja release an air fryer with a built-in AI agent assistant—one that can sense the exact moment you've burned your dinner and place a pizza order instead. Despite all of these new—and sometimes unworthy—AI players, there's no denying that Amazon deserves a seat at the table.

Amazon Bedrock AgentCore—released in July 2025—is AWS's platform for building and deploying AI agents. You have access to several LLMs like OpenAI (via open-weight models), Llama, and Claude, and have the choice of working completely within the AWS framework or collaborating with external tools like LlamaIndex or CrewAI.

The tool has seven primary services, all labeled in catchy one- or two-word phrases. 

• Runtime helps mitigate data leaks via a serverless infrastructure.

• Identify empowers AI tools and workflows to access resources while maintaining data security.

• Gateway allows developers to connect to APIs and external tools.

• Memory allows them to build context-aware AI agents with short- and long-term memory.

• Observability allows developers to monitor AI agents' stats, like session count, latency, duration, token usage, and error rates. 

• Code Interpreter empowers AI agents to write code themselves securely in a sandbox environment to help improve accuracy.

• Browser Tool allows the AI agents to interact with web applications. 

Beyond these primary use cases, AgentCore has some nice features in the fine print. Developers can engage in multi-agent collaboration, implement RAG functionality, modify agent memory and custom code, and orchestrate and execute an AI tool in just a few steps.

Amazon Bedrock AgentCore is slightly late to the AI agent party, but it came with a nice bottle of red wine and an exotic cheese plate—and it's more than worth a shot if you're comfortable in the AWS ecosystem. If you'd like to take the tool beyond Bezos' shadow, connect Amazon Bedrock with Zapier to integrate it into your workflow—like using it to summarize priority emails and send them to your team's Slack, or automatically manage Zendesk ticket priority.

Amazon Bedrock AgentCore pricing: Consumption-based pricing

Build better agent workflows with Zapier

LLMs and chatbots aren't all-knowing beings; they need a little direction and information to follow your instructions, help your team, and assist your customers. You could work with a system like LangChain to build it from scratch—or you could get started with Zapier in minutes.

Zapier is an AI orchestration platform that can help you build comprehensive workflows and transform your operations with AI. 

Related reading:

• What is retrieval augmented generation (RAG) in AI?

• What is multimodal AI? Large multimodal models, explained

• How to create a custom GPT: A beginner's guide

• What is enterprise AI? And how to implement it