Adding Audit Trails to LangChain Agents with Asqav

LangChain agents make decisions autonomously. They call tools, query APIs, and process data. But once they run in production, you need to know exactly what they did. The gap LangChain has callbacks for logging, but no built-in: Cryptographic proof of what happened Policy enforcement to prevent risky actions Compliance-ready audit reports Adding governance pip install asqav Basic integration from asqav import Asqav client = Asqav ( api_key = " sk_... " ) # Create an agent identity agent = client . create_agent ( name = " langchain-research-agent " , algorithm = " ML-DSA-65 " ) # Before each tool call, sign the action def governed_tool_call ( tool_name , tool_input ): # Check policy first sig = client . sign ( agent_id = agent . agent_id , action_type = f " tool: { tool_name } " , action_id = f " { tool_name } - { id ( tool_input ) } " , payload = tool_input ) print ( f " Audit record: { sig . signature_id } " ) # Proceed with the actual tool call return tool . invoke ( tool_input ) Poli