Why use a multi-environment setup for LangChain?

It allows you to test with cheaper models in development while using high-performance models in production, ensures API keys remain secure, and prevents development data from polluting production logs or tracing projects.

What Python libraries are used for configuration?

The setup primarily utilizes Pydantic and Pydantic-Settings for validation and PyYAML for parsing the hierarchical configuration files.

Can I use different LLM providers for different environments?

Absolutely. The included LLM factory pattern makes it easy to switch between providers like OpenAI and Anthropic or different model versions based on the active environment setting.

Does this skill support cloud-native secret management?

Yes, it includes built-in patterns for using GCP Secret Manager in production/staging environments and local .env files for local development.

LangChain Multi-Environment Setup

Name: LangChain Multi-Environment Setup
Author: Brmbobo

byBrmbobo

部署与 DevOps

Configures and manages LangChain applications across development, staging, and production environments using standardized configuration patterns.

关于

This skill automates the creation of a robust, multi-environment architecture for LangChain applications, enabling seamless transitions between development, staging, and production tiers. It implements a hierarchical configuration system using YAML and Pydantic for type-safety, integrates secure secrets management via GCP or local environment variables, and provides environment-aware LLM factories. This ensures that your AI agents use the appropriate models, caching strategies, and tracing projects based on their deployment context, significantly reducing configuration errors and security risks.

主要功能

Pre-configured Docker Compose setup for local development with Redis integration
Type-safe settings management using Pydantic and Pydantic-Settings
Environment-aware LLM factory for switching providers and models dynamically
Hierarchical YAML configuration with environment-specific overrides
0 GitHub stars
Secure secrets handling with GCP Secret Manager and local .env support

使用场景

Managing different LLM models and cost tiers across development and production environments
Implementing secure CI/CD workflows with environment-specific secrets and LangSmith tracing
Scaling a prototype LangChain application to a production-ready enterprise deployment

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add brmbobo/web2podcast langchain-multi-env-setup

For use in Claude.ai and ChatGPT

Download Skill

GitHub

关于