If you're a developer working on an AI app, you've likely come across LangChain and LlamaIndex. These open source frameworks provide essential tools for building complex AI systems, yet they differ in certain aspects.

This article will compare these frameworks in detail, helping you understand their unique features and make informed choices in your AI projects.

Llamaindex, formerly GPT Index, is an open-source data framework designed to develop powerful context-based LLM applications. Optimized for LLM retrieval tasks, it is great for LLM applications that require integrating user-specific data with LLMs (RAG). Here are some of the key features:

• Loading: You can load data from 150+ sources in different formats (API’s, PDF’s, docs, SQL..etc)
• Indexing: You can store and index data in 40+ vector, document, graph, or SQL database providers.
• Querying: You can design complex query flows, with support for retrieval, post- processing and response synthesis. This allows you to build prompt chains, RAG, and agents.
• Evaluations: Recently, Llamaindex joined the evaluation scene, introducing modules for assessing retrieval and response quality.

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Langchain is an open-source framework designed for building end-to-end LLM applications. It provides an extensive suite of components that abstract many of the complexities of building LLM applications. Here are some of the key features:

• Formatting: You can use components to format user input and LLM outputs using prompt templates and output parsers.
• Data Handling: You can use various document loaders, text splitters, retrievers, and embedding models.
• Component Chaining: Using the LangChain Expression Language (LCEL) you can chain all of these components together and build context-aware apps.

Additionally, they introduced LlangSmith, so you can trace what’s going on in your chains, and LangServe, which helps you deploy LangChain chains as a REST API.

*We also wrote about other alternatives to Langchain and you can read about them here.

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To compare these two frameworks, we looked at how broadly and easily they support 9 core capabilities. Our findings show that both are great for learning LLM development and creating proofs of concept. However, they face challenges with more complex applications.

We've detailed these comparisons in the sections below. Feel free to skip to the section that interests you most using the "Table of Contents" on the left, or quickly catch up with the TLDR summary provided below.

Here's how LlamaIndex and LangChain stack up:

Building RAG

LlamaIndex is preferred for seamless data indexing and quick retrieval, making it more suitable for production-ready RAG applications.

Building complex AI workflows

LangChain provides more out-of-the-box components, making it easier to create diverse LLM architectures.

Prompt engineering

‍LangChain offers basic organization and versioning of prompts with its LangSmith feature, though neither framework supports advanced prompt comparison well. Turn to more advanced prompt engineering products for this.

Evaluating AI apps

‍LangChain's LangSmith evaluator suite offers more options than LlamaIndex for general LLM tasks, but it's mostly used for tracing/debugging than evaluations. LlamaIndex only has evals for RAG related metrics. Consider other options here.

Lifecycle management

‍LangChain provides more granular control over debugging, monitoring, and observability with its dedicated platform, LangSmith. However, both frameworks introduce a lot of abstractions which makes it really hard to understand what’s going on below the surface once you start to develop more complex apps.

Safety and guardrails

‍Both frameworks rely on external third-party frameworks for implementing safety measures, with no significant difference in built-in functionalities.‍

Scalability

‍Both frameworks struggle with customization and complexity at scale; developers report that building production-ready AI apps is not easy, as they introduce lots of complexity in cases where you’d be good with 10 lines of code. Turn to products that enable production-ready AI apps. ‍

Community & Improvements

‍Both LlamaIndex and LangChain have active communities, with Langchain moving towards more open-source contributions.‍

Collaborative features

‍LangChain's has built-in support for team collaboration through LangSmith, and LlamaIndex does not. However, it's still not easy to pull in PMs and subject experts to fully participate in the AI development process in LangSmith.

If you're searching for an alternative to Langchain and LlamaIndex that offers greater collaboration, more robust feature evaluation, and the flexibility to develop any AI app ready for production, take a look at Vellum. Discover more here.

Now let’s cover each of these parameters in more detail.

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Advanced prompt engineering components should give you high prompt customization options, and the ability to compare multiple prompts and model scenarios. Let’s see how these two frameworks stack up:

LlamaIndex doesn't have a sandbox for testing prompts. It offers ready-to-use prompt templates, which are good for basic testing but might be limiting for more complex needs, as some users have noted.

LangChain, like Llamaindex, offers simple prompt templates for various uses. It's handy for inspiration and starting out. However, with the new LangSmith feature, it now has a basic prompt playground for testing and adding custom templates to your chains, allowing you to separate prompts from your LangChain code. But, it doesn't support comparing multiple prompt versions for various variants yet.

Verdict: If you want to begin and experiment with pre-made prompt templates, you’re gonna be good with both options. But if you prefer better prompt organization, Langchain lets you track changes with a full commit history, allowing you to version your prompts and organize them into separate components. If you’re looking for advanced prompt comparison option, you’d be better off with tools like Vellum.

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RAG is a technique that integrates external data with LLMs to improve the quality and relevance of generated outputs. When developing production-ready RAG applications, it's crucial to assess each framework's support for data retrieval and integration, as well as its scalability.

LlamaIndex offers seamless data indexing and search querying for quick retrieval. It includes abstractions like RetrieverQueryEngine for managing index retrieval pipelines and Settings for integrating different components like embeddings and LLMs. Additionally, it provides SimpleDirectoryReader for handling various data formats and ContextChatEngine for interactive queries through stored document chunks.

LangChain is rapidly catching up to LlamaIndex in building RAG applications. You can develop various RAG architectures with Langchain, as it supports all needed components for it. However, Langchain can become very complex to maintain once you start scaling your RAG apps, as users report here.

Verdict: Both options are good starting points if you want to build RAG apps. However, if you want to build production-ready RAG, you might lean more towards Llamaindex.

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RAG is not the only architecture that powers AI apps today. There are many other architectures that are custom built depending on product requirements, business needs, and privacy standards. A well built framework should enable developers to build complex workflows that can handle various processing, prompts, llm, API, and logic steps.

LlamaIndex, is naturally a data framework and it mostly focuses on building exceptional RAG architectures.

LangChain offers more out of the box components that enable the creation of various LLM architectures more easily.

Verdict: If you’re looking for greater support of out of the box components that enable you to build complex AI workflows, Langchain might be the better option here.

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Evaluating prompts and models is a key aspect of LLM application development, as it impacts the overall quality and reliability of the system. Comparing both frameworks on performance evaluation informs you about the level of granularity each framework offers for testing and optimizing your LLM applications.

Llamaindex provides a component-based evaluation approach that allows testing individual components of an RAG pipeline, such as the retriever, query engine, and response generation. However, it doesn’t generalize this evaluation approach to handle multiple evaluation metrics and usecases.

Langchain recently introduced their LangSmith evaluator suite, that allows you to test your prompts against hundreds of test cases. To evaluate the prompts, you can use off-the-shelf evaluators that use an LLM as an evaluator in the back, or you can build custom evaluators (hard coded functions that output a score).

Verdict: Llamaindex is great for evaluating RAG components and agents. If your application requires evaluation across a broader array of LLM tasks, LangChain's flexibility and options might be a better fit.

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Adaptability is crucial to building LLM applications, as it allows developers to adapt the framework to their specific project needs and preferences. Comparing the customization features of LlamaIndex and LangChain helps determine which framework offers finer control over the application's data layers, modules, and integration with external tools.

LlamaIndex has very cool features like reordering results with cross encoders, or lost in the middle support, but makes some basic things like sentence transformers unnecessary hard (as reported here).

LangChain, allows easy integration, and provides both RAG and other LLM components out of the box. It probably has more built-in components than Llamaindex, but developers still face the same challenge — many of the components are over engineered, and hard to manage if you want to customize or scale them.

Verdict: Both LlamaIndex and Langchain support many pre-built components, that are really easy to use. If you want a wider palette of pre-built components, you might lean more towards Langchain. However, both frameworks struggle with customization, making them better suited for quick AI proof of concepts or as libraries for specific component usage.

They’re still not favorable by developers for building production-ready AI apps as they introduce a lot of complexity in cases where you’d be good with 10 lines of code. If you want to ship faster, a dedicated LLM product developer tool like Vellum might be more suitable, offering scalability and advanced customizations for both developers and project managers.

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Lifecycle management covers easy debugging, monitoring and high observability for AI apps

Comparing the lifecycle management features of LLM applications is crucial, as it provides insights into each framework's support for:

• Finding errors (debugging ),
• Tracking LLM performance (monitoring),
• Gaining visibility into the application's operations (observability).

Let's see how LlamaIndex and LangChain help you with these tasks:

LlamaIndex offers integrations with tools like Langfuse, DeepEval, OpenLLMetry, and OpenInference for observability. While the predefined components and automated workflows in LlamaIndex simplify development, the high level of abstraction may obscure specific issues, making debugging more challenging.

LangChain provides more granular debugging, monitoring, and observability capabilities through its dedicated observability platform, LangSmith. LangSmith allows you to monitor, test, and debug any LangChain app component. Its "tracing" feature enables detailed logging of intermediate stream results within a system or pipeline.

Verdict: If you want more granular control over your whole AI lifecycle management, LangChain might be a better solution for you.

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Ensuring the ethical and responsible use of LLMs is crucial. Guardrails are rules or constraints that help keep LLM output safe, unbiased, and aligned with your application's purpose. Let's compare how LlamaIndex and LangChain support this:

LlamaIndex integrates with third-party frameworks like GuardrailAI to implement guardrails for LLM applications. It doesn't have any built-in safety features, and the underlying LLM vendors are primarily responsible for managing safety issues, particularly those involving the content that LLMs produce or process. This approach shows a reliance on the safety measures implemented by the LLM providers.

LangChain also integrates with external frameworks like GuardrailAI and Presidio to enhance safety. This dual approach—direct prompt implementation and external framework integration—gives LLM developers a bit more control over safety.

Verdict: This decision might depend on the external third-party framework that you’d like to use, as they both don’t have built-in guardrail functionality.

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Comparing community and continuous improvement is crucial for open-source frameworks, as they’re the main drive that grows them.

LlamaIndex's core software is proprietary, and its team is responsible for development and oversight. However, they recently released a roadmap for the open-source community, and have a massive community.

Langchain is open-sourced, but some components like LangSmith are proprietary and managed by the team. You can find very detailed documentation, and various tutorials created by the community showcasing how popular it is.

Verdict: Both open-source frameworks have very supportive community, consistently incorporating the newest AI features and models in collaboration with the company.

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Developing AI applications ready for production involves collaboration across different teams. Product managers must ensure that AI features are in line with product objectives, developers should facilitate swift development, and subject matter experts need to evaluate if key performance indicators (KPIs) are being met.

LlamaIndex does not have built-in team collaboration features out of the box. Teams using it must manually integrate it into their development lifecycle and workflows. The documentation suggests integrating version control systems (e.g., GitHub) and project management tools (e.g., Jira, Trello) for team collaboration.

While LlamaIndex can be used in a team environment, additional effort is required to set up collaborative tools and processes, which may include integrating with LlamaHub or other third-party solutions.

LangChain has built-in support for team collaboration through LangSmith. LangSmith is designed to support an LLMOps workflow conducive to the collaborative development and operation of LLM applications. Some LangSmith features include shared workspaces for team members and version control for prompts, chains, and other LangChain components.

Verdict: LangChain offers more robust and integrated team collaboration features through Langsmith for an LLMOps workflow that can be adapted to a team's existing processes.

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Vellum is an end-to-end LLM product development platform, that enables your teams to build production-ready AI apps. It enables both developers, PMs, and subject matter experts to collaborate on prompts, evaluate at scale, and ship reliable AI apps.

We’ve worked with companies like Redfin and Drata, and have helped hundreds of other companies to build AI apps.

We rigorously believe that AI development should be done between cross-functional team, in a continuous process that covers:

• Experimentation and validation of ideas;
• Data ingestion for increased accuracy and relevance;
• Lifecycle management via version control and regression testing;
• Continuous improvements by capturing edge cases from production, and fine tuning.

The Vellum product has a user-friendly interface, and developer friendly options to rapidly experiment and connect your system. Most importantly Vellum is designed with the goal to provide the safest and most reliable development of AI features, and we’re proud to be SOC2 Type 2 and HiPAA certified.

If you want to try Vellum, book a demo here, or take a look at our product suite here.