langchain-multi-env-setupClaude Skill

LangChain Multi-Environment Setup

Overview

Configure and manage LangChain applications across development, staging, and production environments.

Prerequisites

• LangChain application ready for deployment
• Access to multiple deployment environments
• Secrets management solution (e.g., GCP Secret Manager)

Instructions

Step 1: Environment Configuration Structure

config/
├── base.yaml                # Shared configuration
├── development.yaml         # Development overrides
├── staging.yaml            # Staging overrides
├── production.yaml         # Production overrides
└── settings.py             # Configuration loader

Step 2: Create Base Configuration

# config/base.yaml
app:
  name: langchain-app
  version: "1.0.0"

llm:
  max_retries: 3
  request_timeout: 30

logging:
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  date_format: "%Y-%m-%d %H:%M:%S"

cache:
  enabled: true
  ttl_seconds: 3600

Step 3: Environment-Specific Overrides

# config/development.yaml
extends: base

app:
  debug: true

llm:
  provider: openai
  model: gpt-4o-mini
  temperature: 0.7
  # Use lower tier in dev

logging:
  level: DEBUG

cache:
  type: memory
  # In-memory cache for local dev

langsmith:
  tracing: true
  project: langchain-dev

# config/staging.yaml
extends: base

app:
  debug: false

llm:
  provider: openai
  model: gpt-4o-mini
  temperature: 0.5

logging:
  level: INFO

cache:
  type: redis
  url: ${REDIS_URL}  # From environment

langsmith:
  tracing: true
  project: langchain-staging

# config/production.yaml
extends: base

app:
  debug: false

llm:
  provider: openai
  model: gpt-4o
  temperature: 0.3
  max_retries: 5
  request_timeout: 60

logging:
  level: WARNING

cache:
  type: redis
  url: ${REDIS_URL}

langsmith:
  tracing: true
  project: langchain-production

monitoring:
  prometheus: true
  sentry: true

Step 4: Configuration Loader

# config/settings.py
import os
from pathlib import Path
from typing import Any, Dict, Optional
import yaml
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings

class LLMConfig(BaseModel):
    provider: str = "openai"
    model: str = "gpt-4o-mini"
    temperature: float = 0.7
    max_retries: int = 3
    request_timeout: int = 30

class CacheConfig(BaseModel):
    enabled: bool = True
    type: str = "memory"
    url: Optional[str] = None
    ttl_seconds: int = 3600

class LangSmithConfig(BaseModel):
    tracing: bool = False
    project: str = "default"

class Settings(BaseSettings):
    environment: str = Field(default="development", env="ENVIRONMENT")
    llm: LLMConfig = Field(default_factory=LLMConfig)
    cache: CacheConfig = Field(default_factory=CacheConfig)
    langsmith: LangSmithConfig = Field(default_factory=LangSmithConfig)

    class Config:
        env_file = ".env"

def load_config(environment: str = None) -> Settings:
    """Load configuration for specified environment."""
    env = environment or os.environ.get("ENVIRONMENT", "development")
    config_dir = Path(__file__).parent

    # Load base config
    config = {}
    base_path = config_dir / "base.yaml"
    if base_path.exists():
        with open(base_path) as f:
            config = yaml.safe_load(f) or {}

    # Load environment-specific overrides
    env_path = config_dir / f"{env}.yaml"
    if env_path.exists():
        with open(env_path) as f:
            env_config = yaml.safe_load(f) or {}
            config = deep_merge(config, env_config)

    # Resolve environment variables
    config = resolve_env_vars(config)

    return Settings(**config)

def deep_merge(base: Dict, override: Dict) -> Dict:
    """Deep merge two dictionaries."""
    result = base.copy()
    for key, value in override.items():
        if key in result and isinstance(result[key], dict) and isinstance(value, dict):
            result[key] = deep_merge(result[key], value)
        else:
            result[key] = value
    return result

def resolve_env_vars(config: Any) -> Any:
    """Resolve ${VAR} patterns in config values."""
    if isinstance(config, dict):
        return {k: resolve_env_vars(v) for k, v in config.items()}
    elif isinstance(config, list):
        return [resolve_env_vars(v) for v in config]
    elif isinstance(config, str) and config.startswith("${") and config.endswith("}"):
        var_name = config[2:-1]
        return os.environ.get(var_name, "")
    return config

# Global settings instance
settings = load_config()

Step 5: Environment-Aware LLM Factory

# infrastructure/llm_factory.py
from config.settings import settings
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_core.language_models import BaseChatModel

def create_llm() -> BaseChatModel:
    """Create LLM based on environment configuration."""
    llm_config = settings.llm

    if llm_config.provider == "openai":
        return ChatOpenAI(
            model=llm_config.model,
            temperature=llm_config.temperature,
            max_retries=llm_config.max_retries,
            request_timeout=llm_config.request_timeout,
        )
    elif llm_config.provider == "anthropic":
        return ChatAnthropic(
            model=llm_config.model,
            temperature=llm_config.temperature,
            max_retries=llm_config.max_retries,
        )
    else:
        raise ValueError(f"Unknown provider: {llm_config.provider}")

Step 6: Environment-Specific Secrets

# infrastructure/secrets.py
import os
from google.cloud import secretmanager

def get_secret(secret_id: str) -> str:
    """Get secret from appropriate source based on environment."""
    env = os.environ.get("ENVIRONMENT", "development")

    if env == "development":
        # Use local .env file in development
        return os.environ.get(secret_id, "")

    else:
        # Use Secret Manager in staging/production
        client = secretmanager.SecretManagerServiceClient()
        project_id = os.environ.get("GCP_PROJECT")
        name = f"projects/{project_id}/secrets/{secret_id}/versions/latest"
        response = client.access_secret_version(request={"name": name})
        return response.payload.data.decode("UTF-8")

# Usage
openai_key = get_secret("OPENAI_API_KEY")

Step 7: Docker Compose for Local Environments

# docker-compose.yml
version: '3.8'

services:
  app:
    build: .
    environment:
      - ENVIRONMENT=development
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    ports:
      - "8080:8080"
    volumes:
      - ./src:/app/src
    depends_on:
      - redis

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"

  # Local development tools
  langsmith-proxy:
    image: langchain/langsmith-proxy:latest
    environment:
      - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}

Output

• Multi-environment configuration structure
• Environment-aware configuration loader
• Secrets management per environment
• Docker Compose for local development

Examples

Running Different Environments

# Development
ENVIRONMENT=development python main.py

# Staging
ENVIRONMENT=staging python main.py

# Production
ENVIRONMENT=production python main.py

Environment Promotion Workflow

# 1. Test in development
ENVIRONMENT=development pytest tests/

# 2. Deploy to staging
gcloud run deploy langchain-api-staging \
    --set-env-vars="ENVIRONMENT=staging"

# 3. Run integration tests
ENVIRONMENT=staging pytest tests/integration/

# 4. Deploy to production
gcloud run deploy langchain-api \
    --set-env-vars="ENVIRONMENT=production"

Error Handling

Error	Cause	Solution
Config Not Found	Missing YAML file	Ensure environment file exists
Secret Missing	Not in Secret Manager	Add secret for environment
Env Var Not Set	Missing .env	Create .env from .env.example

Resources

• Pydantic Settings
• GCP Secret Manager
• 12-Factor App Config

Next Steps

Use langchain-observability for environment-specific monitoring.