| id | SKL-langchain-LANGCHAINPATTERNS |
| name | Langchain Patterns |
| description | LangChain is a framework for building applications powered by LLMs. It helps manage the complexity of prompt chaining, memory, retrieval, agents, and tool use, making it faster to build AI application |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Langchain Patterns
Skill Profile
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Overview
LangChain is a framework for building applications powered by LLMs. It helps manage the complexity of prompt chaining, memory, retrieval, agents, and tool use, making it faster to build AI applications. This skill covers basic setup, structured output, RAG (Retrieval-Augmented Generation), conversational memory, agents with tools, streaming, document loaders, LangSmith integration for production monitoring, and production deployment patterns.
Why This Matters
LangChain is essential for production AI applications because:
- Abstraction: Unified interface for various LLM providers
- Composability: Chain components together easily
- RAG Ready: Built-in retrieval and vector store integrations
- Production: LangSmith for monitoring and debugging
- Ecosystem: Extensive library of integrations and tools
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
Skill Composition
- Depends on: None
- Compatible with: None
- Conflicts with: None
- Related Skills: None
Quick Start
Assumptions
- API keys are available in environment variables
- Vector database is configured and accessible
- Documents are in supported formats (PDF, CSV, text)
