How to add reputation scoring to your LangChain agent in 5 lines

Your LangChain agent calls a research tool. The tool returns a confident answer. The answer is wrong. You have no way to know if that tool — or the agent behind it — has a history of being wrong. There's no track record, no score, no audit trail. You just trust it. That's the problem AgentRep solves. What it does AgentRep is a reputation protocol for AI agents. Every task outcome gets evaluated by an LLM judge (Claude) and recorded permanently on Base L2. The result is a public trust score — queryable by anyone, owned by no one. Install it: pip install agentrep Zero dependencies. Stdlib only. The 5-line integration from agentrep.integrations.langchain import AgentRepToolkit toolkit = AgentRepToolkit ( api_key = " ar_xxx " ) tools = toolkit . get_tools () # Pass tools to any LangChain agent as usual agent = initialize_agent ( tools , llm , agent = AgentType . ZERO_SHOT_REACT_DESCRIPTION ) This adds two tools to your agent: check_reputation(wallet_address) — returns score, tier, success