Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
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---
triggers:
- "langchain architecture"
name: langchain-architecture
description: Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
---
# LangChain Architecture
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
## When to Use This Skill
- Building autonomous AI agents with tool access
- Implementing complex multi-step LLM workflows
- Managing conversation memory and state
- Integrating LLMs with external data sources and APIs
- Creating modular, reusable LLM application components
- Implementing document processing pipelines
- Building production-grade LLM applications
## Core Concepts
### 1. Agents
Autonomous systems that use LLMs to decide which actions to take.
**Agent Types:**
- **ReAct**: Reasoning + Acting in interleaved manner
- **OpenAI Functions**: Leverages function calling API
- **Structured Chat**: Handles multi-input tools
- **Conversational**: Optimized for chat interfaces
- **Self-Ask with Search**: Decomposes complex queries
### 2. Chains
Sequences of calls to LLMs or other utilities.
**Chain Types:**
- **LLMChain**: Basic prompt + LLM combination
- **SequentialChain**: Multiple chains in sequence
- **RouterChain**: Routes inputs to specialized chains
- **TransformChain**: Data transformations between steps
- **MapReduceChain**: Parallel processing with aggregation
### 3. Memory
Systems for maintaining context across interactions.
## Note
Skill content truncated for token efficiency. Full version available in the source repository.
