Langchain vs Llama_index

Langchain and Llama_index are both popular ml inference & agents tools. This page compares their internal architecture, technology stack, data flow patterns, and system behavior — based on automated structural analysis of their source code. They share 2 technologies including pydantic, asyncio.

langchain-ai/langchain

131,015
Stars
Python
Language
10
Components
0.4
Connectivity

run-llama/llama_index

47,705
Stars
Python
Language
8
Components
1.1
Connectivity

Technology Stack

Shared Technologies

pydantic asyncio

Only in Langchain

python threading tenacity pip

Only in Llama_index

fastapi openai nltk pytest mypy black

Architecture Layers

Langchain (5 layers)

Core Layer
Base abstractions and interfaces without third-party dependencies
Classic LangChain
Main framework package with high-level agent orchestration
Integration Partners
Third-party service integrations organized by provider
Specialized Tools
Text splitters, model profiles, and testing utilities
API Management
Deprecation handling, beta features, and backward compatibility

Llama_index (4 layers)

Core Framework
Base abstractions, interfaces, and core functionality for RAG pipelines
Agent System
Workflow-based agents with ReAct, function calling, and code execution capabilities
Integration Layer
Hundreds of integrations for LLMs, embeddings, vector stores, and data readers
Pre-built Components
Ready-to-use RAG patterns and specialized retrieval strategies

Data Flow

Langchain (5 stages)

  1. Agent Planning
  2. Tool Execution
  3. Observation Processing
  4. Response Generation
  5. History Storage

Llama_index (6 stages)

  1. Document Ingestion
  2. Text Processing
  3. Query Processing
  4. Agent Reasoning
  5. Tool Execution
  6. Response Generation

System Behavior

DimensionLangchainLlama_index
Data Pools20
Feedback Loops20
Delays20
Control Points30

Code Patterns

Shared Patterns

abstract base classes

Unique to Langchain

dynamic import system callback chain pattern deprecation management security by default

Unique to Llama_index

plugin architecture event-driven workflows pydantic models

When to Choose

Choose Langchain when you need

  • Unique tech: python, threading, tenacity
  • Loosely coupled, more modular
View full analysis →

Choose Llama_index when you need

  • Unique tech: fastapi, openai, nltk
  • Tighter integration between components
View full analysis →

Frequently Asked Questions

What are the main differences between Langchain and Llama_index?

Langchain has 10 components with a connectivity ratio of 0.4, while Llama_index has 8 components with a ratio of 1.1. They share 2 technologies but differ in 10 others.

Should I use Langchain or Llama_index?

Choose Langchain if you need: Unique tech: python, threading, tenacity; Loosely coupled, more modular. Choose Llama_index if you need: Unique tech: fastapi, openai, nltk; Tighter integration between components.

How does the architecture of Langchain compare to Llama_index?

Langchain is organized into 5 architecture layers with a 5-stage data pipeline. Llama_index has 4 layers with a 6-stage pipeline. They share design patterns: abstract base classes.

What technology does Langchain use that Llama_index doesn't?

Langchain uniquely uses: python, threading, tenacity, pip. Llama_index uniquely uses: fastapi, openai, nltk, pytest, mypy.

Explore the interactive analysis

See the full architecture maps, code patterns, and dependency graphs.

Langchain Llama_index

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Compared on March 25, 2026 by CodeSea. Written by .