LlamaIndex vs LangChain

TL;DR:

• Pick LangChain/LangGraph when you need branching, stateful, or multi-agent control flow. Cyclic graphs, conditional retries, and parallel tool execution are where it earns its keep.

• Pick LlamaIndex when retrieval is the core job since it handles ingestion, indexing, and querying across 160+ data sources out of the box.

• When your primary bottleneck is both, use LlamaIndex for the retrieval layer and LangChain for orchestration around it.

• Both frameworks leave integration infrastructure to you: Composio provides 50,000+ individual tools available and handles auth, token refresh, and schema formatting for either choice.

Agents are ready to do real work. The question is which framework gives them the right foundation: LlamaIndex and LangChain serve different purposes, and Composio acts as action infrastructure across either choice, giving agents one SDK to act across 1,500+ business systems.

LlamaIndex and LangChain serve different purposes: LlamaIndex focuses on data retrieval and RAG pipelines, while LangChain handles agent orchestration and control flow. This article compares them across core design, RAG capabilities, and agentic workflows, then shows how Composio removes the integration debt from either choice.

Core framework philosophies: LlamaIndex vs LangChain

LlamaIndex connects your private data to LLMs and makes it queryable. The framework handles ingestion, indexing, and retrieval for RAG pipelines, document Q&A, and knowledge assistants. Search across a corpus and document Q&A both depend on a first-class ingestion-to-retrieval pipeline, which is exactly what LlamaIndex hands you out of the box. The framework earns its place when retrieval over your own data is the core job.

LangChain orchestrates agent workflows by composing seven component types: Models, Tools, Agents, Memory, Retrievers, Document processing, and Vector Stores. Agents are framed as a model paired with an execution layer, typically assembled via create_agent or a similar constructor. Tools give agents external capabilities, Retrievers and Vector Stores handle external data access, and Memory persists state across turns. Chains are not listed as a component in the current architecture docs; the agent-centric model is the primary abstraction.

Getting this distinction wrong is how teams end up rewriting their stack six months in. LlamaIndex excels at getting your data into a queryable format. LangChain excels at coordinating multi-step agent behavior. Neither is a replacement for the other.

Orchestration models compared

LangChain uses a heavily modular structure where everything is composable. You have separate modules for prompts, models, chains, agents, memory, and callbacks. This modularity gives you fine-grained control but can become complex to maintain once you start scaling.

LlamaIndex workflows are event-driven and step-based. Your application is divided into sections called steps. A step receives an event, does some work, and returns another event. That returned event triggers the next step whose type annotation accepts it. It is simpler than LangChain's graph-based approach but less flexible for complex branching logic.

How LangChain and LlamaIndex handle RAG pipelines

Both frameworks support RAG, but they approach it differently. LlamaIndex treats retrieval as the primary problem. LangChain treats it as one component in a larger orchestration system.

Ingestion pipelines in LlamaIndex vs LangChain

LangChain integrates with document retrieval systems to enhance LLM-generated responses with up-to-date information from knowledge bases. LangChain's retrieval module treats retrieval as one of six core components, not the central focus. You typically use LangChain's document loaders and text splitters, then pass the chunks to a vector store.

LlamaIndex vs LangChain vector stores

LlamaIndex provides native support for vector stores like Pinecone, Weaviate, and Chroma. The framework abstracts the vector store behind a common interface, so you can swap backends without changing your query logic.

LangChain also supports multiple vector stores but treats them as one type of retriever among many. You can use vector stores, keyword search, or hybrid approaches. The flexibility is useful but requires more configuration than LlamaIndex's opinionated defaults.

Retrieval logic: LangChain vs LlamaIndex

LlamaIndex provides a query engine abstraction that handles retrieval and synthesis in one step. You can customize the retrieval strategy (vector similarity, keyword matching, hybrid) and the synthesis strategy (concatenate, refine, tree summarize) independently.

LangChain requires you to build the retrieval logic explicitly using chains. You define a retriever, pass the retrieved documents to a prompt template, and then call the LLM. This gives you more control but also more code to maintain.

Strategies for token window management

LlamaIndex allows you to set chunk sizes to keep the context within the LLM's window. The framework also provides node post-processors that can filter, rerank, or compress retrieved chunks before sending them to the LLM.

LangChain typically requires you to manage context window limits. You can use text splitters to control chunk size, but you need to implement truncation or summarization logic yourself if the retrieved context exceeds the model's limit.

Orchestrating tool calls in your agent stack

Both frameworks support tool calling, but they handle the execution model differently. LangChain uses an agent executor loop. LlamaIndex uses event-driven workflows.

LangChain control flow for agents

LangChain frames the agent as a model paired with an execution layer, typically assembled via create_agent or a similar constructor. The agent runs a loop: the LLM decides which tool to call, the tool executes, the result goes back to the LLM, and the loop repeats until the task is complete. AgentExecutor, the pre-LangGraph way to run this loop, is still functional but has been superseded by LangGraph as the recommended pattern.

One approach teams use for production LangChain agents handling more than a handful of tools at meaningful concurrency: decouple tool execution from the agent loop using an async queue (RQ or Celery on Redis), keep the agent harness stateless, persist conversation state in Redis, and instrument every LLM call, tool call, and memory operation with OpenTelemetry.

Core LlamaIndex agent workflow

LlamaIndex workflows are event-driven and step-based. You define steps that receive events, do work, and return new events. Workflows allow developers to build highly controlled multi-step workflows that can even combine multiple agents.

Agents in LlamaIndex are built on top of workflows. You can use ReAct, function-calling, and tool-use patterns. Workflows provide a structured way to build complex multi-step logic in LlamaIndex.

Optimizing tool selection in production

This is a common failure mode developers report: agents given access to too many tools struggle with selection accuracy, producing hallucinated function calls or choosing the wrong integration for a given task. Tool selection errors can occur when agents have access to multiple similar services, requiring constant prompt engineering to steer behavior.

Composio's Tool Router inspects incoming requests and routes them to the appropriate toolkit based on the user's authenticated connections. When an agent needs to "send an email," Router determines whether to use the Gmail API, Outlook API, or SMTP based on which services the user has connected. This eliminates conditional logic in your agent code and lets users switch providers without changing your implementation, as demonstrated in Composio's LangChain integration.

Multi-step logic in agent frameworks

LangChain's LangGraph represents multi-step logic as a directed cyclic graph. You define nodes (steps) and edges (transitions). This supports branching, merging, and cycling across many steps, including conditional retries and parallel tool execution, which LlamaIndex's event-driven step model does not handle without significant custom logic.

LlamaIndex workflows are simpler: steps receive events and return events. The workflow engine matches event types to step signatures. This is easier to reason about but less flexible for complex control flow.

When to select LlamaIndex vs LangChain

The choice depends on your primary bottleneck. If your problem is getting data into a queryable format, choose LlamaIndex. If your problem is coordinating multi-step agent behavior, choose LangChain.

LlamaIndex for data-heavy agents

Use LlamaIndex when your core job is retrieval over your own data. Search across a corpus, RAG, document Q&A, and knowledge assistants all benefit from LlamaIndex's ingestion-to-retrieval pipeline. The framework handles chunking, embedding, indexing, and retrieval with minimal configuration.

Best scenarios for LangChain orchestration

Use LangChain when you need complex agent control flow. Multi-step workflows, human-in-the-loop approval, state persistence, and time-travel debugging all benefit from LangGraph's graph-based model. LangChain is the better choice when your agent needs to coordinate multiple tools, handle errors, and maintain state across long-running conversations.

Using LlamaIndex with LangChain

The two can also run together: a common production shape uses LlamaIndex for the retrieval layer and LangChain or a custom loop for the orchestration around it. You might use LlamaIndex to parse, index, and retrieve data from documents, and LangChain to orchestrate an agent that uses that retrieved context with tools, prompts, and model calls.

A common production pattern that emerges from running both frameworks is this split: LlamaIndex for retrieval, LangGraph for orchestration.

Scaling LlamaIndex and LangChain for production

Both frameworks are production-ready, but they scale differently. LlamaIndex scales by adding more data sources and optimizing retrieval. LangChain scales by adding more tools and managing state across distributed agent runs.

Debugging agent failures at scale

Agents that work in demos fail silently in production. Token expiry, race conditions, and inconsistent API responses cause failures without clear error signals. An agent that passes single-run testing breaks after continuous operation when tokens expire mid-conversation.

LangGraph provides checkpointing and debugging capabilities that can help trace execution flow. LlamaIndex workflows provide event logs that help trace the sequence of steps. Neither framework solves the underlying integration problem: API rate limits, schema drift, and execution reliability across business systems.

Scaling LlamaIndex vs LangChain agents

LangChain agents scale horizontally by running multiple agent instances behind a load balancer. LangGraph's checkpointing layer persists conversation state in a store of your choice, including Redis or PostgreSQL, so any instance can resume a conversation. LlamaIndex agents scale by adding more worker processes to handle ingestion and retrieval. The query engine is stateless, so you can scale it independently of the ingestion pipeline.

Both frameworks face challenges when you add many tools: agents struggle with selection accuracy as the tool count increases. This is where Composio's Tool Router helps by filtering tools based on the user's authenticated connections.

Composio holds SOC 2 Type II and ISO 27001 certifications, providing documented compliance posture for security review. This removes a pain point developers commonly report: security reviews stalling deployment for weeks because the auth implementation couldn't be documented clearly.

Try Composio with your framework and follow the quickstart at Composio.

FAQs

Is LlamaIndex better than LangChain for RAG applications?

LlamaIndex is better for RAG applications because it provides ingestion, indexing, and retrieval out of the box. LangChain requires you to build the retrieval pipeline using chains and retrievers.

Can I switch from LangChain to LlamaIndex without rewriting everything?

You can switch the retrieval layer without rewriting your orchestration logic if you use LlamaIndex for retrieval and LangChain for orchestration. A common production pattern combines both: LlamaIndex for the retrieval layer and LangChain for orchestration around it.

Which framework has better tool integration support?

LangChain has more built-in integrations, but Composio provides 1,500+ production-ready integrations for both frameworks through provider packages.

Do I need Composio if I'm already using LangChain or LlamaIndex?

Frameworks provide orchestration, not production-ready integrations. Composio handles OAuth, token refresh, schema formatting, and tool maintenance so you don't have to.

What happens at scale with each framework?

LangChain scales by adding more AgentExecutor instances and persisting state in Redis or PostgreSQL. LlamaIndex scales by adding more worker processes for ingestion and retrieval.

Key terms glossary

RAG (Retrieval-Augmented Generation): A technique that retrieves relevant documents from a knowledge base and includes them in the LLM's context to improve response accuracy.

Agent orchestration: The coordination of multi-step agent behavior, including tool calling, state management, and error handling.

Tool Router: Composio's routing layer that inspects incoming requests and routes them to the appropriate toolkit based on the user's authenticated connections.

Provider package: A framework-specific adapter that transforms Composio tools into the native format your framework expects, such as LangChain tools or LlamaIndex tools.

Connect Link: A URL that Composio returns when an agent needs access mid-conversation. The user authenticates once, and credentials persist for all future sessions.

Vector store: A database optimized for storing and querying high-dimensional vectors (embeddings) for semantic search.

AgentExecutor: LangChain's legacy agent loop (pre-LangGraph) that repeatedly calls the LLM, executes tools, and returns results until the task is complete. LangGraph is now the recommended pattern for state and control flow, with create_react_agent as a common constructor.

Workflow: LlamaIndex's event-driven, step-based execution model where steps receive events, do work, and return new events.