Agent skill · software engineering · aiskillstore
langchain-architecture
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
Why this skill is useful
Adds executable scripts and specific patterns for building complex LLM applications using LangChain that are not widely documented.
What it needs
Requires langchain installed locally. About 5k 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. Do not use this skill when The task is unrelated to langchain architecture You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. If detailed examples are required, open resources/implementation-playbook.md. Use this skill when 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/langchain-architecture