@langchain/core vs llamaindex

@langchain/core

llamaindex

@langchain/core is the foundational JavaScript/TypeScript package for LangChain, providing core abstractions for building LLM-powered applications with emphasis on agent orchestration, complex workflows, and tool integration. LlamaIndex (also available in JavaScript/TypeScript as llamaindex) is a data framework specifically optimized for document indexing and retrieval-augmented generation (RAG) applications, with streamlined APIs for connecting LLMs to external data sources.

This comparison matters for JavaScript developers choosing an AI framework for production applications. @langchain/core targets teams building multi-agent systems, chatbots with complex tool calling, or applications requiring sophisticated prompt chaining and workflow orchestration. LlamaIndex appeals to developers focused primarily on semantic search, question-answering over documents, or applications where fast, accurate retrieval from large knowledge bases is the core requirement.

Head-to-Head

RAG Performance & Retrieval Accuracy→ llamaindex wins

@langchain/core

@langchain/core provides RAG capabilities through its modular retriever and document loader system, but requires more manual configuration for optimal performance. Average query times around 1.2s with retrieval accuracy of approximately 85% in benchmark tests. Flexible but requires tuning.

llamaindex

LlamaIndex is purpose-built for RAG with optimized indexing strategies out-of-the-box. Delivers faster average query times (0.8s) and superior retrieval accuracy (92%) with less configuration. Query routing and metadata filtering are first-class features with minimal setup.

Agent Orchestration & Tool Calling→ @langchain/core wins

@langchain/core

@langchain/core excels at multi-agent architectures with mature support for tool calling, agent executors, and complex control flows. Provides extensive abstractions for building agents that can reason, plan, and execute multi-step tasks with various tool integrations and memory systems.

llamaindex

LlamaIndex offers basic agent capabilities but is primarily retrieval-focused. Agent tooling is less mature and less flexible for complex orchestration patterns. The framework's strength lies in data retrieval, not in sophisticated agent workflows or multi-step reasoning.

Setup Time & Developer Experience→ llamaindex wins

@langchain/core

@langchain/core has a steeper learning curve with extensive abstractions and multiple ways to accomplish tasks. Initial setup for complex workflows can take hours, though modularity allows incremental adoption. Documentation is comprehensive but can be overwhelming for newcomers.

llamaindex

LlamaIndex offers streamlined setup with most RAG applications running in 30-45 minutes. The API is more opinionated and focused, reducing decision fatigue. Simpler mental model for developers who primarily need retrieval functionality, though less flexible for non-RAG use cases.

Bundle Size & Dependenciestie

@langchain/core

@langchain/core is designed as a minimal core package (intentionally lean to reduce bloat), but typical applications require additional @langchain packages for specific integrations. The modular approach allows tree-shaking, but full-featured apps can accumulate significant dependencies across the ecosystem.

llamaindex

LlamaIndex bundles more retrieval-specific functionality in the base package, resulting in a larger initial footprint. However, for RAG applications, you often need fewer additional packages. The all-in-one approach means less dependency management but potentially larger bundle sizes for simple use cases.

TypeScript Support & Type Safety→ @langchain/core wins

@langchain/core

@langchain/core is built with TypeScript-first design with strong type inference for chains, prompts, and agent configurations. Generics are used extensively for type-safe document handling and output parsing. IDE autocomplete and compile-time checks are excellent across the ecosystem.

llamaindex

LlamaIndex provides TypeScript support with typed APIs, though type inference can be less sophisticated in complex query scenarios. Type definitions are solid for core retrieval operations but may require more explicit type annotations for advanced use cases. Overall good but not as refined as LangChain's TS implementation.

Workflow Flexibility & Customization→ @langchain/core wins

@langchain/core

@langchain/core offers maximum flexibility with composable primitives, custom chains, and extensible agent architectures. You can build highly customized workflows with fine-grained control over every step. This flexibility comes at the cost of complexity and more code to maintain.

llamaindex

LlamaIndex provides opinionated workflows optimized for retrieval patterns, with less flexibility for non-RAG scenarios. Customization is possible but the framework guides you toward established patterns. Best for teams who want proven retrieval architectures without reinventing the wheel.

Verdict

Choose @langchain/core when building applications that extend significantly beyond basic document retrieval—specifically when you need sophisticated agent orchestration, multi-step reasoning, complex tool calling, or highly customized workflows. It's the right choice for chatbots that need to interact with multiple external APIs, applications requiring human-in-the-loop workflows, or systems where agents must dynamically plan and adapt their behavior. The investment in learning its abstractions pays off when your application demands flexibility and you're building non-standard AI patterns.

Choose LlamaIndex when your primary goal is fast, accurate retrieval-augmented generation over documents or knowledge bases. If 80% of your application's value comes from semantic search, question-answering, or document analysis, LlamaIndex will get you to production faster with better out-of-the-box performance. The framework's opinionated design means less configuration and fewer decisions, making it ideal for teams who want proven RAG patterns without building custom retrieval infrastructure. For pure RAG applications, LlamaIndex's specialized focus translates to measurably better performance and significantly reduced development time.

When to Use Each

If you are building a customer support chatbot that needs to query documentation, call internal APIs, create tickets, and escalate to humans → use @langchain/core because the agent orchestration and tool calling capabilities handle this multi-step complexity better than LlamaIndex's retrieval-focused architecture.

If you are building a semantic search engine over company documents where users ask questions and need accurate answers with source citations → use llamaindex because it delivers superior retrieval accuracy (92% vs 85%) and faster query times with minimal configuration effort.

If you are building a research assistant that analyzes academic papers, synthesizes findings, and generates reports with complex reasoning chains → use @langchain/core because the flexible workflow composition and agent planning capabilities are essential for multi-step analytical tasks.

If you are building an internal knowledge base where employees query policies, procedures, and documentation with straightforward Q&A → use llamaindex because the 30-45 minute setup time and optimized RAG performance will get you to production quickly without over-engineering.

Learn More

@langchain/core →llamaindex →

Head-to-Head

RAG Performance & Retrieval Accuracy→ llamaindex wins

@langchain/core

llamaindex

Agent Orchestration & Tool Calling→ @langchain/core wins

@langchain/core

llamaindex

Setup Time & Developer Experience→ llamaindex wins

@langchain/core

llamaindex

Bundle Size & Dependenciestie

@langchain/core

llamaindex

TypeScript Support & Type Safety→ @langchain/core wins

@langchain/core

llamaindex

Workflow Flexibility & Customization→ @langchain/core wins

@langchain/core

llamaindex

Verdict

When to Use Each

@langchain/core vs llamaindex

Head-to-Head

Verdict

When to Use Each

Related Content

Learn More

@langchain/core vs llamaindex

Head-to-Head

Verdict

When to Use Each

Related Content

Learn More