A Comprehensive Comparison of LLM Chaining Frameworks

A Comprehensive Comparison of LLM Chaining Frameworks

As the advantages and potential of learning language models (LLMs) become more evident, an increasing number of organizations are seeking ways to integrate them into their business processes. LLM chaining frameworks offer a practical and cost-efficient approach for companies to test the capabilities of language models and discover how to best utilize generative AI to achieve their long-term goals.

This guide delves into the concept of LLM chaining, examines various LLM chaining frameworks, and provides insights on choosing the right framework to meet your company’s specific needs.

Understanding LLM Chaining

LLM chaining refers to the process of connecting large language models to other applications, tools, and services to generate the most effective output from a given input. By linking an LLM to additional components, you can create feature-rich generative AI applications, known as LLM chains, tailored to your specific use case.

A common example of LLM chaining is connecting a pre-trained language model to an external data source to enhance its responses, known as retrieval-augmented generation (RAG). In this setup, the user’s prompt is used to query a data source, such as a vector database, for relevant information. This information is then retrieved and used to augment the model’s response, improving its accuracy and relevance.

An LLM chaining framework is a collection of components, libraries, and tools that enable developers to create LLM chains. By providing all the necessary elements to build highly functional LLM applications, development teams can save significant time and resources when developing AI-powered solutions.

Applications of LLM Chaining Frameworks

LLM chaining frameworks can be used to create a variety of generative applications, including:

  • Chatbot Assistants: These frameworks provide components to build advanced chatbots. This includes connecting to a data store for accessing domain-specific or proprietary information and incorporating short or long-term memory for recalling previous conversations.

  • Document Analysis: An LLM chain can significantly increase the maximum size of documents that a model can analyze. By connecting a model to tools such as text splitters and chunkers, LLM applications can process larger documents than a foundation model can by default.

  • Text Summarizers: An LLM chain that connects to external information sources enables applications to accurately summarize texts from specialized knowledge domains.

  • Semantic Search: LLM chaining frameworks offer the data retrieval, indexing, and storage mechanisms needed to build high-performing search applications.

  • Question Answering (QA) Applications: These frameworks provide integrations with various data types and sources, making them ideal for developing LLM-powered QA applications.

Exploring Different LLM Chaining Frameworks

Let’s take a closer look at some leading LLM chaining frameworks, their key components and features, their pros and cons, and the best scenarios for their use.

1. LangChain

LangChain is an open-source framework available in Python and JavaScript, enabling developers to combine LLMs with other tools and systems to create diverse end-to-end AI applications. Developers can choose from a vast selection of off-the-shelf modules or create LLM chains using the LangChain Expression Language (LECL), a simple declarative programming language that streamlines the development lifecycle of LLM-based applications.

The LangChain ecosystem includes:

  • LangChain Libraries: The core of the framework, containing modules (primitives) and a runtime for creating chains and agents. Key library modules include:

    • Model I/O: Manages interactions with a wide selection of language and chat models, including input prompt management and output parsing.

    • Chains: Wrappers that link modules together, including individual modules and pre-composed chains. LangChain offers two types of ready-made chains:

      • Generic Chains: General-purpose chains that link various application components.

      • Utility Chains: Chains designed for specific tasks, e.g., APIChain, which uses an LLM to convert a query into an API request, execute the request, receive a response, and pass the response back to the LLM.

    • Retrieval: Components facilitating the storage and access of external information used by LLM-powered applications, including embedding models, document retrievers, text splitters, and vector databases.

    • Agents: Tools that receive instructions from user input or LLM output and use them to perform predefined actions automatically.

    • Memory: Modules that provide LLM applications with short and long-term memory capabilities, enabling them to retain a persistent state during operation, e.g., conversation history for a chatbot.

  • LangChain Templates: A collection of reference architectures covering various use cases that developers can use as starting points for custom AI applications.

  • LangServe: Tools for deploying an LLM chain as a REST API.

  • LangSmith: A development platform integrated with LangChain for streamlined debugging, testing, and monitoring of production-grade LLM applications.

Pros and Cons of LangChain


  • Comprehensive Library: LangChain offers an extensive library of ready-made LLM chains and modules, enabling developers to craft solutions for a wide range of use cases.

  • Active Development Community: LangChain has a large, active user base, making it easier to find solutions when troubleshooting or seeking advice. A larger community also means more contributions of custom LLM chains, tools, and functionalities; the C# implementation of LangChain is a prime example.

  • Extensive Integrations: LangChain supports hundreds of integrations, including compatibility with a wide array of LLMs from leading vendors, HuggingFace, and Cohere; vector databases like ChromaDB and Pinecone; and cloud service providers such as GCP and AWS.

  • Powerful Prompt Engineering: LangChain’s prompt templates simplify the process of precisely defining input and output formats, allowing for more efficient and robust LLM applications.


  • Limited Search and Retrieval Capabilities: Despite its extensive library, LangChain’s data retrieval, indexing, and querying functionalities are not as powerful as LlamaIndex’s plug-and-play search and storage capabilities.

When Should You Use LangChain?

LangChain is an excellent choice for developers who are new to LLM chains or LLM application development. Its array of out-of-the-box chains and straightforward setup make it ideal for experimentation and prototyping custom LLM chains.

2. LlamaIndex

LlamaIndex is a versatile Python and Typescript-based framework designed for chaining LLMs to various external data sources. Its robust data connection and indexing capabilities make it especially suitable for creating data-centric LLM applications, such as those leveraging retrieval-augmented generation (RAG) to enhance or optimize language model outputs.

Key Components of the LlamaIndex Framework

  • LlamaHub: A vast library of data connectors that facilitate the automatic ingestion of unstructured, structured, and semi-structured data from over 100 different sources, including databases and APIs.

  • Indexing: Components that enable the creation of data indices, which LLM-based applications can navigate to expedite data retrieval. LlamaIndex allows for easy index updates, eliminating the need to recreate the index from scratch when adding new data.

  • Engines: Core components that connect LLMs to data sources, allowing access to the documents within. LlamaIndex features two types of engines:

    • Query Engines: Retrieval interfaces that augment model output with documents accessed via your data connectors. These engines support querying multiple data sources and selecting the appropriate source depending on the query, such as a SQL or vector database.

    • Chat Engines: By tracking conversation history and retaining input for subsequent queries, chat engines enable multi-message querying with data.

  • Agents: Automatic reasoning engines that process user input and internally determine the best course of action to deliver the optimal response.

  • LlamaPacks: Templates of real-world RAG applications created with LlamaIndex.

Pros and Cons of LlamaIndex


  • Data Processing and Retrieval: LlamaIndex’s standout feature is its ready-made data connectors, indices, and query engines, which offer a straightforward yet powerful way for an LLM to access external knowledge and provide it with memory.

  • Diverse Integrations: LlamaIndex can integrate with various third-party tools and services, including LangChain, offering developers additional tools and functionality beyond LlamaIndex’s libraries.

  • Multi-Modal Support: LlamaIndex provides data connectors for images in addition to text documents.


  • Smaller and Less Diverse Library: While LlamaIndex excels in data processing and retrieval, its overall flexibility and versatility are less than that of LangChain.

  • Less Intuitive: Though not particularly challenging to learn, LlamaIndex isn’t as user-friendly as other LLM chaining frameworks.

When Should You Use LlamaIndex?

Compared to LangChain: While LangChain offers a broader, general-purpose component library, LlamaIndex shines in data collection, indexing, and querying. This specialization makes LlamaIndex the ideal choice for use cases that require semantic search and retrieval applications and those involving large and/or complex datasets.

3. Haystack

Haystack is an open-source Python framework designed for developing custom LLM applications, similar to LlamaIndex. It provides a library of components specifically for creating applications with semantic search and retrieval capabilities.

Key Features of the Haystack Framework

  • Nodes: The building blocks of applications in Haystack. Nodes are individual components designed to perform different tasks or roles within a system. Haystack offers a comprehensive collection of ready-made nodes and allows for custom component creation. Examples of nodes include:

    • Prompts: Used to pass input to LLMs or specify a prompt template.

    • Reader: Scans texts within stored documents to find answers to queries.

    • Retriever: Obtains documents relevant to the input query from a database.

    • Crawler: Scrapes text from web pages and creates documents.

    • Decision: Classifies queries and directs them to different nodes based on their content.

  • Pipelines: Haystack's implementation of a chain, where nodes are combined to form an end-to-end application. Custom pipelines can be composed from nodes and other pipelines, or you can start with a ready-made pipeline from the library. Examples include:

    • DocumentSearchPipeline: Returns documents that best match a given query.

    • ExtractiveQAPipeline: Answers questions from information extracted from stored documents.

    • SearchSummarisationPipeline: Summarizes retrieved documents used to produce query outputs.

  • Agents: Components that use existing nodes or pipelines to iteratively generate the best possible output for a given query. Agents are useful in scenarios where multiple iterations and more knowledge collection are required to arrive at a correct answer. An agent first generates a "thought," divides it into smaller steps, selects a component, and feeds it text input. Based on the output quality, the agent either displays it to the user or repeats the process for a more accurate response.

Pros and Cons of Haystack


  • Easy-To-Read Documentation: Haystack’s documentation is concise and well-organized, making it easier for developers to understand its components and features. Tutorials and cookbooks further flatten the learning curve.

  • Simple Framework: Haystack comprises nodes, pipelines, and agents, each with a relatively small number of classes, making it easier for developers to find the appropriate component for their use case.


  • Smaller User Base: Haystack doesn’t have as active a developer community as LangChain or LlamaIndex, resulting in fewer contributions of community-made components and potential difficulties in finding answers when stuck.

When Should You Use Haystack?

While Haystack doesn’t offer the breadth of functionality found in LangChain, it provides powerful semantic search and data retrieval capabilities. Its compact and intuitive nature makes it an excellent choice for developing simpler search and indexing-based LLM applications and quick proof-of-concept prototyping.

4. AutoGen

AutoGen is a Python-based LLM chaining framework developed through collaboration between Microsoft and the Universities of Washington, Penn State, and Xidian. It allows developers to create LLM applications through the configuration of multiple AI agents that communicate to undertake tasks like data retrieval and code execution.

Key Features of the AutoGen Framework

  • Multi-Agent Conversations: Using pre-built or custom agents to pass data between each other with optional human intervention. AutoGen agents fall into three categories:

    • UserProxyAgent: Collects input from the user and passes it to other agents for processing and execution.

    • AssistantAgent: Receives data from UserProxyAgents and other AssistantAgents and performs pre-configured tasks.

    • GroupChatManager: Manages communication between agents in an application.

  • Diverse Conversational Patterns: AutoGen supports a variety of conversational patterns for mapping complex workflows. Conversations can be configured by the number of agents, their conversational autonomy (including human input), and the conversational topology. Dynamic conversational capabilities adjust the agent topology according to the conversation flow and the agents’ ability to complete tasks, which is useful for intricate use cases where interaction patterns can’t be anticipated in advance.

Pros and Cons of AutoGen


  • Simplicity: The abstraction of components as agents makes it easier to understand the AutoGen framework, especially for non-technical personnel, including management and key stakeholders in digital transformation projects.

  • Intuitive Agent Customization: Agents are configured through user-friendly interfaces and conversational commands, enabling customization with minimal coding, if any at all.


  • Harder to Debug: Debugging multi-agent applications can be complex due to the various interdependencies between agents.

When Should You Use AutoGen?

AutoGen is ideal for creating LLM applications focused on multi-agent interactions, automation, and conversational prompting. It allows development teams to experiment with the utility of autonomous agents and how they could be integrated into existing workflows.

Here's a comparison of Langchain, LlamaIndex, Haystack, and Autogen in the form of a table:

Programming Language SupportPython, Java, JavaScript, Go, Rust, C++, Ruby, PHP, Swift, KotlinPythonPythonN/A (Web-based)
Data Source TypesText files, databases, web pagesWeb pages, PDFs, Microsoft Office documentsElasticsearch, PostgreSQL, MySQL, MongoDBN/A (Uses existing data sources)
Search AlgorithmsTF-IDF, BM25Triton, AnnoyElasticsearch, Whoosh, FAISSN/A (Uses existing search engines)
Question Answering CapabilitiesYesBasic question answering with predefined templatesAdvanced question answering using BERT, RoBERTa, ELECTRALimited to generating text from prompts
Integration OptionsREST API, SDKs for various programming languagesREST APIDjango integration, Flask integrationIframe or embeddable widget
Pricing ModelOpen source; community support availableFree trial, then paid plans starting at $49/monthOpen source; community support availableFree plan available, then paid plans starting at $10/month
Primary Use CaseBuilding apps with LLMsIndexing and querying datasetsSearch and QA systemsAutomated content and workflow gen
IntegrationStrong, multiple LLMs and toolsMultiple data sources, LLMsElasticsearch, OpenAI, other backendsVarious APIs and data sources
Language SupportMulti-language (based on LLM)Multi-language (based on LLM)Multi-languageMulti-language
Ease of UseHighModerateHighHigh
Unique FeaturesModular design, customizable workflowsEfficient indexing, complex queriesEnd-to-end search and QA frameworkAutomation focus, non-developer friendly
Community SupportStrongGrowingStrongGrowing


The emergence of LLM chaining frameworks has been pivotal in the generative AI revolution, significantly leveling the playing field for AI adoption. Now, generative AI application development is not restricted to companies with vast resources and highly skilled personnel. With frameworks like LangChain, LlamaIndex, Haystack, and AutoGen, organizations of all sizes can harness the power of LLM-based systems and tools, creating end-to-end applications at lower costs, in less time, and without needing highly proficient machine learning developers.