Been seeing a lot of questions about production safety for LangChain agents so wanted to share what we use. The problem: once a LangChain agent has tool access in production, there’s no built-in way to intercept and block dangerous actions before they execute. Here’s a lightweight approach using Vaultak: pip install vaultak from vaultak import Vaultak vt = Vaultak(api_key="vtk_...") with vt.monitor("my-agent"): result = agent.invoke({"input": user_input}) Every tool call now gets risk-scored before execution. You can add policies to block specific actions: vt.policy.create({ "name": "no-prod-deletes", "action": "delete", "resource": "production_*", "effect": "deny", "priority": 1 }) There’s also a free scanner at AI Agent Risk Scanner, Vaultak if you want to check your agent’s risk profile without writing any code first. Full guide with more examples: vaultak.com How to Secure LangChain Agents in Production A practical guide to adding runtime security to LangChain agents. Happy to answer questions about the implementation. 3 posts - 2 participants Read full topic