LangChain has become a popular framework for building LLM-powered applications, thanks to its flexible approach to chaining prompts, connecting tools, and managing memory. However, as GenAI projects become more complex and developer needs continue to evolve, LangChain is not always the most suitable option.
In this guide, you'll discover more than 20 LangChain alternatives designed for a wide range of use cases, including real-time agent frameworks, visual builders, and enterprise-level orchestration tools. Whether you're prototyping a chatbot or scaling a production system, this guide will help you find the right platform and understand when it makes sense to choose something other than LangChain.
LangChain is an open-source framework designed to help developers build powerful applications using LLMs like OpenAI's GPT-4, Anthropic's Claude, Google's Gemini, Mistral, and Meta's Llama. While LLMs are incredibly capable on their own, most real-world applications require more than just prompting a model. They need access to company data, third-party tools, memory, and logic. That's where LangChain comes in.
LangChain provides a modular, programmable layer around LLMs that lets developers:
While it's often described as a wrapper around LLMs, LangChain is more like an orchestration engine, allowing developers to turn static prompts into dynamic, multi-step workflows. It supports both Python and JavaScript offering flexibility to backend and frontend teams alike.
At the core of LangChain are building blocks like:
With over 600 integrations available, from vector databases and cloud platforms to CRMs and DevOps tools, LangChain makes it easier to build production-ready GenAI apps that are data-aware, tool-using, and context-sensitive.
LangChain makes it easy to build apps where LLMs can access private or proprietary data stored in documents, databases, or knowledge bases. This is useful when the model alone doesn't "know" the answer.
Examples:
LangChain's agent framework allows LLMs to decide what actions to take, what tools to use, and in what sequence — making them useful for dynamic, open-ended tasks.
Examples:
Sometimes a single LLM isn't enough. LangChain can coordinate multiple tools, APIs, and even different models to complete a task, with logic that spans across steps.
Examples:
LangChain enables memory and context retention across conversations, so your AI doesn't have to start from scratch every time.
Examples:
LangChain can be used to extract, transform, and enrich data using LLMs, especially from unstructured sources.
Examples:
LangChain is a great choice for prototyping and building data-aware, LLM-powered apps. But as your needs scale or shift toward enterprise, real-time, or mission-critical workloads, its limitations start to show.
Here's where LangChain can fall short — and why some developers are turning to frameworks like Akka instead.
LangChain is ideal for rapid experimentation. You can spin up a working demo quickly, and the ecosystem makes it easy to connect tools, models, and data sources.
"The value they have is it's an easier experience — you follow a tutorial and boom, you already have durable execution, and boom, you already have memory. But the question is, at what point are you going to be like, 'Now I'm running this in production, and it doesn't work very well?' That's the question," says Richard Li, thought leader and advisor on Agentic AI.
LangChain's memory and workflow systems are simple and developer-friendly, but they lack the maturity and rigor needed for critical systems.
Akka, on the other hand, is built on an actor-based concurrency model with years of engineering behind it. It's been tested at scale, across industries, and is designed to be resilient under pressure.
LangChain's architecture is not well-suited for applications that rely on continuous, high-volume data. It's designed around request-response patterns, which work well for static inputs but not for scenarios involving streaming media, telemetry, or sensor-driven environments.
"If you're doing video or audio or real-time high-volume data — no way you can use any of these frameworks. You've got to use something like Akka."
Akka's event-driven design enables it to handle live data and dynamic workloads efficiently, making it a better fit for systems that need constant responsiveness.
LangChain includes basic components for memory and workflow execution. These features are great for prototyping, but they offer few guarantees in terms of durability or consistency.
"They're trying to compete with two categories: memory and durable workflow... but there are dedicated companies that have existed for a long time for a reason."
LangGraph extends LangChain by offering a state-machine-like workflow model, but it may still struggle in scenarios requiring long-term session tracking, failover resilience, or compliance with enterprise-grade SLAs.
LangChain supports Python and JavaScript/TypeScript. These languages are accessible and widely used, but they may not meet the performance or security requirements of high-throughput or regulated applications.
"You're just not going to build a streaming video platform in Python. You're just not."
If your organization relies on JVM-based systems, or needs strict type safety and performance tuning, frameworks like Akka — built in Scala and Java — offer more control and efficiency.
LangChain excels at helping teams experiment with agentic AI and build early-stage applications. However, it isn't a comprehensive system framework.
"We're not the thing you toy with... we're more of a system when you've done the research and you're ready to get serious," says Darin Bartik, CMO at Akka.
Akka is built for teams that already know what they want to build and are ready to ensure it scales reliably. It's a better fit once you've moved beyond exploration and need a dependable system foundation for GenAI or other intelligent services.
LangGraph is a relatively new addition to the LangChain ecosystem that deserves special attention when considering LangChain alternatives. LangGraph is an orchestration framework built on top of LangChain, specifically designed for creating complex, stateful agent systems with more granular control than traditional LangChain agents.
| Feature | LangChain | LangGraph |
|---|---|---|
| Architecture | Sequential chains and basic agents | Graph-based workflows with support for cycles |
| State Management | Basic memory mechanisms | Enhanced state persistence with checkpoint capabilities |
| Workflow Design | Linear processes | Complex conditional logic with feedback loops |
| Agent Structure | Predefined agent patterns | Flexible multi-agent coordination with explicit transition control |
LangGraph represents an evolution in the LangChain ecosystem rather than a complete alternative. Many developers use both frameworks together, starting with LangChain for basic components and leveraging LangGraph for orchestration as their applications grow more complex.
As this guide from LangChain puts it, "LangGraph is built on top of LangChain and completely interoperable with the LangChain ecosystem. It adds new value primarily through the introduction of an easy way to create cyclical graphs. This is often useful when creating agent runtimes."
Here are 25 LangChain alternatives you can use for your next GenAI application.
Frameworks and platforms that enable building autonomous or multi-agent systems with planning, memory, and tool use.
Akka is a high-performance platform for building scalable, resilient agentic AI applications. Its actor-based architecture supports high-throughput, low-latency systems, making it ideal for cloud-native microservices, real-time data processing, and event-driven applications. Akka simplifies horizontal scaling, fault tolerance, and state recovery, with features like Akka Cluster, Sharding, and Persistence ensuring real-time performance and reliable data pipelines.
Designed for cloud-native environments, Akka supports flexible deployments, from serverless to self-managed Kubernetes, allowing teams to build fault-tolerant systems without complex infrastructure management.
Key Features
Pricing
Pricing is flexible based on your hosting requirements. Serverless plans start at $0.25 per hour. Bring your own cloud starts at $750/month + $0.15/Akka hour on major providers. Finally, self-hosted plans start at $5000/month and + $0.15/ Akka hour.
Akka Vs. LangChain
| Feature / Capability | LangChain | LangGraph | Akka |
|---|---|---|---|
| Primary Use Case | LLM app prototyping, RAG, tool orchestration | Complex agent systems, stateful workflows, multi-agent coordination | Real-time systems, distributed AI, enterprise backends |
| Programming Languages | Python, JavaScript/TypeScript | Python, JavaScript | Java, Scala (JVM ecosystem) |
| Durable Workflow Support | Lightweight, experimental (via LangGraph) | Enhanced with state machine support and persistence | Mature, production-grade (proven at scale) |
| Memory/State Management | In-memory or basic persistence | Graph-based state management with persistent storage options | Strong state management with supervision and fault tolerance |
| Performance | Optimized for single-session LLM tasks | Improved for complex agent workflows but still oriented toward LLM tasks | Designed for high throughput and parallelism |
| Real-Time Capability | Limited (batch-oriented) | Moderate (supports cycles and stateful applications) | Excellent (suitable for streaming, IoT, video, etc.) |
| Security & Type Safety | Dynamic typing, higher security risk | Same as LangChain (built on top of it) | Strong typing, better suited for regulated industries |
| Best For | Startups, prototypes, RAG chatbots | Complex agent systems, multi-agent coordination, human-in-the-loop workflows | Enterprises, real-time agents, mission-critical AI |
| Maturity Level | Fast-moving, evolving | Newer than LangChain, still evolving | Enterprise-ready, battle-tested |
AutoGen is a Microsoft framework for building scalable multi-agent AI systems using Python. What makes AutoGen special is that it's heavily integrated into the Microsoft ecosystem.
It integrates with OpenAI models out of the box and is the ideal choice for people looking for a framework that easily scales and can work in multi-language environments. It even includes an extension to work with LangChain tools.
Key Features
Pricing
This is a free/open-source product designed to sell more Azure services.
AutoGPT platform focuses on helping users create AI agents that augment their abilities, enabling more productivity at work. The AutoGPT framework uses Python and Typescript, giving you flexibility when creating your advanced agents. You also get tools to make your agents reliable and predictable giving you peace of mind when you deploy them.
Key Features
Pricing
The actual code is free and open-source, but the cloud infrastructure is currently in beta with no pricing available.
CrewAI is a multi-agent platform for enterprise users. CrewAI creates low-code tools that help enterprises create advanced AI agents with any LLM backend. It also provides support for deploying to a variety of cloud providers. With over 1,200 integrations, CrewAI is a flexible option if you are looking for a way to create enterprise-scale AI agents with minimal to no code.
Key Features
Pricing
Free and open source edition with enterprise pricing based on individual needs and requirements.
Griptape is a modular open-source Python framework to help developers build LLM-powered applications. It provides powerful primitives to help developers create conversational and event-driven AI applications.
Key Features
Pricing
While there is no charge for running the AI tools on your own machine, there is a cloud hosting service that charges $1 per GB for ETL, $0.01 per retrieval query for RAG, and $0.25 per hour to RUN. There is also custom enterprise pricing.
Haystack is a comprehensive framework designed to build production-ready LLM applications, RAG pipelines, and sophisticated search systems, akin to IBM Watson. It allows you to experiment with current AI models through a modular architecture, offering the scalability needed for large applications.
Key Features
Pricing
The framework is free and open-source, but Deepset has a drag-and-drop program called Deepset Studio.
Langroid is a framework that simplifies the management of multiple underlying LLMs, enabling the creation of powerful AI agents. It emphasizes efficient task delegation across various agents and offers straightforward methods for interacting with vector stores and models.
The framework is written in Python, providing a convenient solution for people looking to build agentic applications without complex backend programming.
Key Features
Pricing
Langroid is a free and open-source framework for developers with Python programming experience.
Built to structure LLM pipelines, function calling, and multi-step reasoning workflows.
GradientJ is an all-in-one platform for building and managing LLM applications. It supports data integration and makes it easy to compare prompt performance across models to find the best fit.
Key Features
Pricing
Currently a free framework with more functionality coming in the future.
Outlines is a Python library built with a focus on reliable text generation with LLMs. It mainly supports OpenAI, but there is also support for llama.cpp, exllama2, vllm, transformers, and other models. It provides robust prompting for various language models and is compatible with every auto-regressive model.
Key Features
Pricing
It is a free and open-source Python library that can be extended by any developer.
Langdock is an integrated platform for your developers and enterprise users. It contains all the tools developers need to build and deploy custom AI workflows. For enterprise users, it provides specific AI assistants, search tools, and AI chatbots to help users be more productive.
Key Features
Pricing
The Langdock platform includes a free seven-day trial and a €25 per month business plan. There are also custom enterprise plans with customer-specific pricing.
Semantic Kernel is Microsoft's lightweight dev kit for creating AI agents using C#, Python, or Java. It's essentially a middleware layer on which you can build enterprise-grade AI agents with a variety of plugins. It is modular and extensible, making it easy to customize the framework to your specific requirements.
Key Features
Pricing
As with most open-source frameworks from Microsoft, the software is free and there to incentivize you to use the Azure Cloud platform.
Txtai is a database for LLM orchestration, language model flows, and semantic search. You can think of it as a tool for turbocharging your autonomous agents. It is an Embeddings database that can work with audio, images, video, text, and documents. It has Python bindings and all the defaults necessary to get up and running quickly.
Key Features
Pricing
This is a free and open-source library with API bindings for other languages. The framework is free but you will pay for cloud hosting based on your chosen platform.
No-code or low-code tools for visually building LLM apps, agents, or workflows.
AgentGPT is an interface wrapper in front of ChatGPT. It streamlines the process of creating AI agents by providing easy-to-understand templates for developers to create robust GenAI apps. AgentGPT makes the process as easy as entering a name and a goal.
Key Features
Pricing
There is a free trial with limited capabilities. The $40 pro plan gives you access to 30 agents per day and the latest ChatGPT model. There is also an enterprise plan with custom pricing.
Flowise is an LLM orchestration and AI agent creation tool for users who require a low-code solution. It provides all the functionality you need for LLM orchestration through over 100 integrations, even working with LangChain. There are API, SDK, and Embed options, providing flexibility for every type of developer.
Key Features
Pricing
Pricing starts at $35 per month and goes up to $65 per month for the pro package. There is an enterprise package with custom pricing.
Langflow provides a robust drag-and-drop interface over a Python framework that lets you create powerful agents that can connect to a variety of LLMs, APIs, and databases. It gives you the tools needed to focus on creativity instead of application architecture.
Key Features
Pricing
The actual framework is free and open source, but you do need to pay for cloud hosting, which will depend on your usage.
N8n platform focuses on giving you flexibility and control over how you create your AI agents. There is a drag-and-drop interface for people who want to create AI agents without using code. There is also a coding framework for developers who want maximum control.
Key Features
Pricing
The starter plan is $20 per month when paid for annually and the Pro plan is $50 per month when paid for annually. There is also a custom enterprise plan with custom pricing.
Rivet offers a visual programming environment for creating AI agents with LLMs. It provides a streamlined space for designing, debugging, and collaborating with other developers within your organization. This makes it an ideal solution for building sophisticated AI agents on LLMs, even without extensive software development experience.
Key Features
Pricing
Free open-source desktop app with support for MacOS, Windows, and Linux.
SuperAGI provides a flexible platform for users to create embedded AI agents for a variety of industries. It is a unified platform, providing all the associated integrations you need to automate sales, marketing, IT, and engineering tasks. It lets you create AI agents using a visual programming language, making the process faster and easier.
Key Features
Pricing
SuperAGI has simplified pricing, coming in at $100/pack/month for 10,000 credits.
Tools and platforms focused on building RAG pipelines with document indexing, chunking, and retrieval.
LlamaIndex focuses on providing tools to handle data on top of LLMs. This lets organizations extract, analyze, and act on complex data. LlamaIndex also provides an excellent cloud service for companies looking for an integrated solution that can do it all. It comes with a robust document parser to help you get more from your enterprise documents.
Key Features
Pricing
The framework is free, but there is a paid cloud service that charges $1.00 per 1,000 credits in North America and $1.50 per 1,000 credits in Europe.
Tools and platforms used to train, fine-tune, or deploy models (foundational infrastructure rather than orchestration).
Hugging Face provides a central repository for models, datasets, and applications. There are available Spaces that allow you to share ML applications and demos with people around the world. Hugging Face is an entire ecosystem for developers and enterprises, offering the tools necessary to collaborate with others.
Key Features
Pricing
There is an available Pro account for $9 per month that provides advanced features and Spaces Hardware where you can rent CPU and GPU time.
TensorFlow is Google's framework for creating machine learning models. TensorFlow makes it possible for experts and beginners to create a variety of ML models. It provides the functionality needed to load, process, and transform data to train your ML models.
Key features
Pricing
The TensorFlow framework is free to use but Google provides a variety of cloud hosting options for people looking to create and train ML models. Google Colab is also a platform for people looking to run ML models.
Utilities and dev tools to help with prompt iteration, evals, and agent debugging.
Humanloop provides a platform that lets you develop, evaluate, and observe your AI applications for maximum performance. You get access to a platform for shipping AI products faster and with fewer problems. The Humanloop platform provides an intuitive UI for developing advanced AI products.
Key features
Pricing
Humanloop provides a free trial version with 50 eval runs and 10k logs/month. There is also an enterprise version with custom pricing and features built with the customer's needs in mind.
Mirascope provides LLM abstractions that are modular, reliable, and extensible. This library is an excellent option for developers looking for a simplified process of working with multiple LLMs. It is compatible with LLMs from providers like OpenAI, Antropic, Google, Groq, Mistral, and more.
Key features
Pricing
This tool is a free and open-source library with the only thing needed being Python programming experience.
Priompt is a small open-source prompting library that uses priorities to set the context window. It emulates libraries like React, making it an excellent choice for seasoned JavaScript developers who want to get into creating AI agents. The creator advocates for treating prompt design the same way we design websites, which is why the library works the way it does.
Key features
Pricing
This is a small, free, and open-source library.
LangChain has played a key role in making LLM app development more accessible, but it's no longer the only option (or always the best one). As GenAI projects grow in complexity, developers now have access to a wide range of tools tailored for specific needs, from rapid prototyping to enterprise-scale deployments.
For teams that need real-time responsiveness, strong memory handling, or distributed processing, Akka offers a compelling alternative. Built on the JVM and designed for durability and performance, it's better suited for production environments where reliability is non-negotiable.