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backend-patterns

Standardize FastAPI and LangChain backend services with architectural patterns.

Updated Apr 28, 2025
Search Tags:#architecture#fastapi#python#backend#langchain#api development#agent tools
One-click install
npx skills add https://github.com/tim-o-private/llm-agent --skill backend-patterns-tim-o-private
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backend-patterns
Source: https://github.com/tim-o-private/llm-agent/tree/main/.claude/skills/backend-patterns
Command: npx skills add https://github.com/tim-o-private/llm-agent --skill backend-patterns-tim-o-private

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide and set of best practices for building robust and scalable backend services using FastAPI and LangChain, ensuring code quality, maintainability, and adherence to architectural principles.

Core Features & Use Cases

  • Architectural Guidance: Understand the recommended structure for routers, services, and database interactions.
  • Tool Integration: Learn the pattern for creating and integrating custom agent tools with LangChain.
  • API Development: Follow recipes for adding new API endpoints and background job types.
  • Use Case: When developing a new feature that requires a new API endpoint and a corresponding agent tool, consult this Skill to ensure the implementation follows established patterns for services, routers, Pydantic models, and tool registration.

Quick Start

Use the backend-patterns skill to understand the recipe for adding a new agent tool.

Frequently Asked Questions about backend-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure a FastAPI and LangChain backend for maintainability?▼

FastAPI and LangChain backend structure should separate routers, services, and database interactions. This Skill provides a standardized framework using service layer patterns, dependency injection, and Pydantic validation to ensure code quality and architectural adherence.

What is the recipe for adding a new agent tool to a LangChain backend?▼

Adding a new agent tool to a LangChain backend requires following a specific recipe for tool creation and registration. This Skill details the pattern for integrating custom tools alongside service layer components and Pydantic models.

How do I implement authentication and Pydantic validation in FastAPI services?▼

Authentication and Pydantic validation in FastAPI services are implemented using established architectural patterns. This Skill offers best practices for dependency injection and validation to secure endpoints and maintain data integrity.

Can I use executor caching and prompt template rendering with FastAPI?▼

Executor caching and prompt template rendering can be used with FastAPI to optimize LangChain operations. This Skill addresses these techniques directly, offering guidance on avoiding common pitfalls during API development.

What are common pitfalls when developing FastAPI and LangChain backend services?▼

Common pitfalls in FastAPI and LangChain backend services often involve mismanaged dependency injection and improper service layer structuring. This Skill identifies these issues and offers recipes for adding new API endpoints and background job types correctly.

Best way to add new API endpoints and background job types in a Python backend?▼

The best way to add API endpoints and background job types in a Python backend is following standardized architectural recipes. This Skill provides step-by-step guidance for integrating routers, services, and Pydantic models effectively.

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