Building an intelligent assistant requires much more than just calling a large language model. Developers need tools to orchestrate prompts, manage context and memory, integrate knowledge sources and handle conversations securely. In the rapidly evolving ecosystem of agent frameworks there are dozens of options. This article compares five popular libraries—LangChain, Haystack, LlamaIndex, Rasa and OpenAI Function Calling—and summarises their benefits, drawbacks and basic integration steps.

LangChain

Overview. LangChain is a modular Python library designed to simplify the creation of chain‑of‑thought pipelines and conversational agents. It organises applications around components such as prompts, models, memory and chains. A prompt defines how to query a large language model, while a model encapsulates the chosen LLM provider. A memory object stores conversation history so the agent can produce context‑aware responses, and chains connect these pieces into workflows. LangChain also includes agents that select which tool to call (e.g., web search, calculator) based on the user's intent and the current context. The library supports many LLM providers, embeddings and vector stores, and provides evaluation tools such as LangSmith and LangServe.

Pros. LangChain's modular design makes it flexible and easy to extend with new tools. Its memory subsystem enables multi‑turn conversations where the assistant retains and uses previous messages. Built‑in chains and agents accelerate prototyping, and community‑contributed connectors allow integration with various data sources and vector stores.

Cons. The library is still evolving, and many APIs are experimental. Because it aims to support many use cases, choosing the right components can be confusing for beginners. The emphasis on flexibility means there is no single "best practice" pipeline; developers must understand the building blocks to configure an effective agent.

Integration. LangChain can be installed via pip (pip install langchain). A minimal setup involves creating a PromptTemplate, selecting an LLM (such as OpenAI's models or Anthropic's Claude), defining a ConversationBufferMemory to store messages, and wrapping these in a ConversationChain. For more complex tasks, developers can define custom tools (functions with descriptions), wrap them with Tool objects and build an agent that uses these tools. LangChain's documentation includes examples for document loaders, retrieval‑augmented generation and agent execution.

Haystack

Overview. Haystack is an open‑source Python framework for building retrieval‑augmented generation (RAG) pipelines and LLM‑powered applications. It uses components—such as retrievers, generators, document stores and pipelines—to assemble systems for question answering, chatbots, summarisation and semantic search. Haystack integrates with various language models and vector stores; examples include Elasticsearch, OpenSearch, Pinecone, Qdrant and LLM APIs from OpenAI, Cohere and Anthropic. The framework emphasises a technology‑agnostic and explicit design, making each component transparent and easy to debug.

Pros. Haystack is purpose‑built for RAG, so it excels at combining an LLM with a search index. Its components can be composed explicitly, enabling fine‑grained control and clear debugging of each step. Since Haystack is open source, developers can self‑host pipelines or deploy them on deepset Cloud.

Cons. Haystack has fewer first‑party integrations than some competing frameworks, so connecting new data sources may require additional development. The explicit nature of pipelines can increase boilerplate code compared with more opinionated frameworks. Because it focuses on RAG, it provides fewer utilities for general chat‑agent orchestration and conversation flow.

Integration. Install Haystack via pip (pip install haystack-ai). A typical pipeline begins by loading a document store (e.g., Elasticsearch or Qdrant), populating it with content, and creating a Retriever that performs similarity search. A Generator (LLM) then formulates answers based on retrieved documents. Pipelines can be executed synchronously or deployed as REST APIs. Haystack's modular architecture makes it straightforward to switch between LLM providers or vector stores.

LlamaIndex

Overview. LlamaIndex (formerly GPT Index) is a free and open‑source framework for connecting external data to large language models. It abstracts the process into four stages: connectors ingest data from files, APIs or databases; indexes organise the ingested data into structures such as vector indexes or tree indexes; engines provide query interfaces for tasks like Q&A, chat or semantic search; and agents can take actions based on the data. LlamaIndex integrates with more than forty vector stores and LLMs and over 160 data sources, and supports tasks like structured extraction and summarisation.

Pros. The framework's clear separation between ingestion, indexing and querying makes it easy to build retrieval systems. LlamaIndex works with many storage back‑ends and can persist indexes to disk or cloud storage, supporting incremental updates and deployment to serverless environments. Its evaluation module helps assess the quality of responses, and the community maintains many connectors.

Cons. Like LangChain, LlamaIndex is rapidly evolving. Documentation sometimes lags behind new features, and building advanced agents may require combining it with other libraries. While it excels at retrieval and indexing, it does not include built‑in dialogue management; developers must integrate a conversational framework.

Integration. Install via pip (pip install llama-index). Begin by creating a SimpleDirectoryReader or other connector to load documents. Use an Index (e.g., VectorStoreIndex) to embed and store the data. To query, instantiate a QueryEngine or ChatEngine and pass the user question; the engine will retrieve relevant chunks and generate a response. Indexes can be saved to disk using index.storage_context.persist() for reuse. For advanced agents, combine LlamaIndex with an orchestration library such as LangChain.

Rasa

Overview. Rasa is a framework for building scalable, high‑trust conversational AI assistants. It combines large language models with deterministic flows to ensure compliance and safety. Flows enable designers to define business logic for conversations; they can be dynamically adjusted based on context, and the assistant automatically decides when to follow the flow or use the underlying LLM. Rasa provides built‑in protections against hallucination and prompt injection, and it can be run on‑premise to meet strict data governance requirements.

Pros. Rasa's open‑source nature eliminates vendor lock‑in and encourages a large community of contributors. It uses machine‑learning models to improve intent recognition and conversation quality, scales to high message volumes, and supports text and voice channels. Because it is not tied to a specific LLM, organisations can select models that meet their privacy and compliance needs.

Cons. Rasa has a steep learning curve; developers must understand Python and machine learning concepts. Setting up and training a chatbot can be time‑consuming and resource‑intensive, and out‑of‑the‑box features are limited. While Rasa excels at deterministic workflows, chatbots may struggle with nuanced emotional cues and require additional customisation.

Integration. Installation depends on the edition. The enterprise edition requires a licence key and an API key for the chosen LLM provider. Developers can deploy via Python or Docker; the open‑source core can be installed with pip install rasa. After installation, create a project with rasa init, define intents, entities and flows in YAML files, and train the model. You can integrate external LLMs through the CALM architecture, which balances deterministic flows with generative capabilities. Rasa provides connectors for popular messaging platforms and voice assistants.

OpenAI Function Calling

Overview. OpenAI's function calling feature provides a structured way for language models to interact with external services. Developers define JSON schemas for functions, then include these definitions in their API request. When the model determines that a function is needed to answer a user's question, it returns a JSON object with the function name and arguments. The developer executes the function (e.g., look up weather data or update a database) and sends the result back to the model for a final response.

Pros. Function calling enables real‑time data access, task automation and integration with existing APIs. The structured JSON output reduces hallucinations and makes results easier to parse. It allows developers to extend the model's capabilities without training a specialised agent.

Cons. The approach requires predefined functions and schemas; adding new capabilities involves writing additional code. Handling errors and mapping user intent to functions can be complex, and misinterpretations may occur. Each call introduces latency and increases token usage. Security and privacy must be considered, since the model may output function arguments containing sensitive data.

Integration. Use the OpenAI Chat API and supply a functions parameter describing the names, descriptions and JSON schemas of available functions. In the conversation loop, inspect the model's response: if it returns a function_call, parse the arguments, execute the corresponding function in your environment, and send a new message containing the function's result. Repeat this process until the model provides a final answer without a function call. Although this mechanism is simple, orchestrating multiple calls or combining function calling with retrieval often requires an additional agent framework such as LangChain.

Choosing the Right Library

Selecting a library depends on the type of assistant and your team's expertise. LangChain and LlamaIndex excel at orchestrating chains and retrieval over diverse data sources; they are ideal for prototypes or research projects where flexibility is crucial. Haystack offers a pragmatic, RAG‑focused toolkit that pairs well with search‑heavy applications. Rasa prioritises reliability, flows and on‑premise deployment, making it suitable for regulated industries. OpenAI Function Calling is a lightweight option for developers who want to enrich LLMs with real‑time data and specific actions without adopting a full framework. As the ecosystem matures, these tools are increasingly interoperable—allowing developers to combine the best components for their particular use case.