---

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-a…[redacted]
APP_ENV=development

# .env.staging
ANTHROPIC_API_KEY_STAGING=sk-a…[redacted]
APP_ENV=staging

# .env.production (stored in secret manager, not files)
ANTHROPIC_API_KEY_PROD=sk-a…[redacted]
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.