--- name: langchain-prod-checklist-853cd4c513a1 description: | Execute LangChain production deployment checklist. Use when preparing for production launch, validating deployment readiness, or auditing existing production LangChain applications. Trigger with phrases like "langchain production", "langchain prod ready", "deploy langchain", "langchain launch checklist", "production checklist". allowed-tools: Read, Write, Edit, Bash(python:*) version: 1.0.0 license: MIT author: Jeremy Longshore --- # LangChain Production Checklist ## Overview Comprehensive checklist for deploying LangChain applications to production with reliability, security, and performance. ## Prerequisites - LangChain application developed and tested - Infrastructure provisioned - CI/CD pipeline configured ## Production Checklist ### 1. Configuration & Secrets - [ ] All API keys in secrets manager (not env vars in code) - [ ] Environment-specific configurations separated - [ ] Fallback values for non-critical settings - [ ] Configuration validation on startup ```python from pydantic_settings import BaseSettings from pydantic import Field, SecretStr class Settings(BaseSettings): """Validated configuration.""" openai_api_key: SecretStr = Field(..., env="OPENAI_API_KEY") model_name: str = "gpt-4o-mini" max_retries: int = Field(default=3, ge=1, le=10) timeout_seconds: int = Field(default=30, ge=5, le=120) class Config: env_file = ".env" settings = Settings() # Validates on import ``` ### 2. Error Handling & Resilience - [ ] Retry logic with exponential backoff - [ ] Fallback models configured - [ ] Circuit breaker for cascading failures - [ ] Graceful degradation strategy ```python from langchain_openai import ChatOpenAI from langchain_anthropic import ChatAnthropic primary = ChatOpenAI(model="gpt-4o-mini", max_retries=3) fallback = ChatAnthropic(model="claude-3-5-sonnet-20241022") robust_llm = primary.with_fallbacks([fallback]) ``` ### 3. Observability - [ ] Structured logging configured - [ ] Metrics collection enabled - [ ] Distributed tracing (LangSmith or OpenTelemetry) - [ ] Alerting rules defined ```python import os # LangSmith tracing os.environ["LANGCHAIN_TRACING_V2"] = "true" os.environ["LANGCHAIN_API_KEY"] = settings.langsmith_api_key os.environ["LANGCHAIN_PROJECT"] = "production" # Prometheus metrics from prometheus_client import Counter, Histogram llm_requests = Counter("langchain_llm_requests_total", "Total LLM requests") llm_latency = Histogram("langchain_llm_latency_seconds", "LLM latency") ``` ### 4. Performance - [ ] Caching configured for repeated queries - [ ] Connection pooling enabled - [ ] Timeout limits set - [ ] Batch processing for bulk operations ```python from langchain_core.globals import set_llm_cache from langchain_community.cache import RedisCache import redis # Production caching with Redis redis_client = redis.Redis.from_url(os.environ["REDIS_URL"]) set_llm_cache(RedisCache(redis_client)) ``` ### 5. Security - [ ] Input validation implemented - [ ] Output sanitization enabled - [ ] Rate limiting per user/IP - [ ] Audit logging for all LLM calls ```python from langchain_core.runnables import RunnableLambda def validate_input(input_data: dict) -> dict: """Validate and sanitize input.""" user_input = input_data.get("input", "") if len(user_input) > 10000: raise ValueError("Input too long") return input_data secure_chain = RunnableLambda(validate_input) | prompt | llm ``` ### 6. Testing - [ ] Unit tests for all chains - [ ] Integration tests with mock LLMs - [ ] Load tests completed - [ ] Chaos engineering (failure injection) ```python # pytest.ini [pytest] markers = unit: Unit tests integration: Integration tests load: Load tests ``` ### 7. Deployment - [ ] Health check endpoint - [ ] Graceful shutdown handling - [ ] Rolling deployment strategy - [ ] Rollback procedure documented ```python from fastapi import FastAPI from contextlib import asynccontextmanager @asynccontextmanager async def lifespan(app: FastAPI): # Startup print("Warming up LLM connections...") yield # Shutdown print("Cleaning up...") app = FastAPI(lifespan=lifespan) @app.get("/health") async def health_check(): return {"status": "healthy", "model": settings.model_name} ``` ### 8. Cost Management - [ ] Token counting implemented - [ ] Usage alerts configured - [ ] Cost allocation by tenant/feature - [ ] Budget limits enforced ```python import tiktoken def estimate_cost(text: str, model: str = "gpt-4o-mini") -> float: """Estimate API cost for text.""" encoding = tiktoken.encoding_for_model(model) tokens = len(encoding.encode(text)) # Approximate pricing (check current rates) cost_per_1k = {"gpt-4o-mini": 0.00015, "gpt-4o": 0.005} return (tokens / 1000) * cost_per_1k.get(model, 0.001) ``` ## Deployment Validation Script ```python #!/usr/bin/env python3 """Pre-deployment validation script.""" def run_checks(): checks = [] # Check 1: API key configured try: settings = Settings() checks.append(("API Key", "PASS")) except Exception as e: checks.append(("API Key", f"FAIL: {e}")) # Check 2: LLM connectivity try: llm = ChatOpenAI(model="gpt-4o-mini") llm.invoke("test") checks.append(("LLM Connection", "PASS")) except Exception as e: checks.append(("LLM Connection", f"FAIL: {e}")) # Check 3: Cache connectivity try: redis_client.ping() checks.append(("Cache (Redis)", "PASS")) except Exception as e: checks.append(("Cache (Redis)", f"FAIL: {e}")) for name, status in checks: print(f"[{status}] {name}") return all("PASS" in status for _, status in checks) if __name__ == "__main__": exit(0 if run_checks() else 1) ``` ## Resources - [LangChain Production Guide](https://python.langchain.com/docs/guides/productionization/) - [LangSmith](https://docs.smith.langchain.com/) - [Twelve-Factor App](https://12factor.net/) ## Next Steps After launch, use `langchain-observability` for monitoring. --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-prod-checklist/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License (with OpenAI/Anthropic Rider) Copyright (c) 2026 Jeffrey Emanuel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: ADDITIONAL RIDER / RESTRICTION (OpenAI / Anthropic): This rider is part of the "conditions" of this License. In the event of any conflict between this rider and any other portion of this License, this rider controls. "Restricted Parties" means OpenAI, L.L.C.; Anthropic, PBC; any of their respective Affiliates; and any person or entity acting directly or indirectly on behalf of, for the benefit of, or under the direction of any of the foregoing (including any officer, director, employee, contractor, agent, consultant, service provider, or representative). Notwithstanding any other provision of this License, no rights are granted to any Restricted Party. Any purported license, sublicense, assignment, transfer, or other permission to any Restricted Party is null and void absent the express prior written permission of Jeffrey Emanuel. 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Any breach of this rider automatically and immediately terminates the permissions granted by this License. Upon termination, you must immediately cease all use and distribution of the Software and any Derivative Works and destroy all copies under your control. You agree that a breach of this rider would cause irreparable harm and that Jeffrey Emanuel may seek injunctive or other equitable relief to enforce this rider, in addition to any other remedies available at law. To the maximum extent permitted by applicable law, the prevailing party in any action to enforce this rider shall be entitled to recover reasonable attorneys' fees and costs. For purposes of this rider, "Affiliate" means any entity that directly or indirectly controls, is controlled by, or is under common control with the specified party. "Control" means ownership of more than 50% of the voting securities or other ownership interest, or the power to direct management or policies by contract or otherwise. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-prod-checklist/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Backend Developer, DevOps / SRE, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: Backend Tools: Langchain Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 105a8e7c9629fe79e5060e9694146b15878a79bcde990697bf5b1e0bf58d4aed