Agent skill · software engineering · aiskillstore
langchain-architecture
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.
Why this skill is useful
Adds executable scripts and specific integration patterns for building complex LLM applications using LangChain that are not widely known.
What it needs
Requires langchain installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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. Memory Types: ConversationBufferMemory: Stores all messages ConversationSummaryMemory: Summarizes older messages ConversationBufferWindowMemory: Keeps last N messages EntityMemory: Tracks information about entities VectorStoreMemory: Semantic similarity retrieval 4. Document Processing Loading, transforming, and storing documents for retrieval. Components: Document Loaders: Load from various sources Text Splitters: Chunk documents intelligently Vector Stores: Store and retrieve embeddings Retrievers: Fetch relevant documents Indexes: Organize documents for efficient access 5. Callbacks Hooks for logging, monitoring, and debugging. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/langchain-architecture--5c1f15