---

name: anth-multi-env-setup description: 'Configure Claude API across dev, staging, and production environments

with isolated keys, model routing, and spend controls per environment.

Trigger with phrases like "anthropic environments", "claude multi-env",

"anthropic staging setup", "claude dev vs prod config".

' allowed-tools: Read, Write, Edit, Bash(npm:*), Grep version: 1.7.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• ai
• anthropic compatibility: Designed for Claude Code

---

Anthropic Multi-Environment Setup

Overview

Configure isolated Claude API environments with per-env API keys, model selection, and spend controls using Anthropic Workspaces.

Environment Configuration

# config.py
import os
from dataclasses import dataclass

@dataclass
class ClaudeConfig:
    api_key: str
    model: str
    max_tokens: int
    max_retries: int
    timeout: float
    monthly_budget_usd: float

CONFIGS = {
    "development": ClaudeConfig(
        api_key=os.environ["ANTHROPIC_API_KEY_DEV"],
        model="claude-haiku-4-20250514",       # Cheap for dev
        max_tokens=256,
        max_retries=1,
        timeout=15.0,
        monthly_budget_usd=10.0,
    ),
    "staging": ClaudeConfig(
        api_key=os.environ["ANTHROPIC_API_KEY_STAGING"],
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        max_retries=2,
        timeout=30.0,
        monthly_budget_usd=50.0,
    ),
    "production": ClaudeConfig(
        api_key=os.environ["ANTHROPIC_API_KEY_PROD"],
        model="claude-sonnet-4-20250514",
        max_tokens=4096,
        max_retries=5,
        timeout=120.0,
        monthly_budget_usd=5000.0,
    ),
}

def get_config() -> ClaudeConfig:
    env = os.getenv("APP_ENV", "development")
    return CONFIGS[env]

Anthropic Workspaces (Key Isolation)

Create separate Workspaces in console.anthropic.com:

Workspace	Purpose	Rate Limit Tier
dev	Development & testing	Tier 1
staging	Pre-production validation	Tier 2
production	Live traffic	Tier 3+

Each workspace has independent API keys, usage tracking, and rate limits.

Environment Files

# .env.development
ANTHROPIC_API_KEY_DEV=sk-ant-api03-dev-...
APP_ENV=development

# .env.staging
ANTHROPIC_API_KEY_STAGING=sk-ant-api03-stg-...
APP_ENV=staging

# .env.production (stored in secret manager, not files)
ANTHROPIC_API_KEY_PROD=sk-ant-api03-prd-...
APP_ENV=production

Client Factory

import anthropic

def create_client() -> anthropic.Anthropic:
    config = get_config()
    return anthropic.Anthropic(
        api_key=config.api_key,
        max_retries=config.max_retries,
        timeout=config.timeout,
    )

Per-Environment Model Override

# Development: always use Haiku (cheapest)
# Staging: use production model for accuracy testing
# Production: use configured model

def get_model(override: str | None = None) -> str:
    if override:
        return override
    return get_config().model

Error Handling

Issue	Cause	Fix
Dev key used in prod	Wrong env loaded	Validate key prefix matches environment
Staging rate limited	Low tier workspace	Upgrade staging workspace tier
Cost overrun in dev	No budget guard	Add per-env spend limits

Prerequisites

• Define the environment inventory, workspace/key ownership, model policy, rate and spend budgets, data classification, and promotion approver.
• Provision separate least-privileged credentials through a secret manager; production secrets must not exist in repository files, shell history, examples, or CI logs.
• Prepare synthetic fixtures, environment isolation tests, a canary route, and a rollback configuration before changing any workspace or client factory.

Instructions

1. Map each environment to exactly one approved Anthropic workspace and secret-manager reference. Validate environment identity at startup and fail closed on a missing or mismatched key.
2. Load configuration through the environment-specific client factory, pin model and API settings, and enforce per-environment token, rate, timeout, retry, data, and destination limits.
3. Run authentication, cross-environment isolation, budget, and request-shape tests with synthetic fixtures. Capture only aggregate pass/fail and usage metadata.
4. Promote a reviewed artifact from staging to a small internal canary before production. Require owner approval and verify no production traffic or data can reach non-production workspaces.
5. On drift, leaked scope, or failed health checks, disable the route, restore the previous environment mapping, rotate affected credentials, and retain a redacted receipt.

Output

Produce an environment receipt containing environment/workspace classes, config and artifact digests, model policy, isolation and synthetic-test results, canary/approval state, secret rotation status, retention, and rollback reference. Exclude API keys, endpoint tokens, prompts, responses, and member identifiers.

Examples

Run a synthetic fixture-request-001 through development and staging with separate keys, assert workspace_crossing=0; production_key_in_nonprod=0; content_logged=0, and record canary=internal; approval=pending. Promotion remains blocked until the owner approves the staging receipt.

Resources

• Workspaces
• Console

Next Steps

For monitoring, see anth-observability.