Jev 作为 LangChain 中间件接入 agent 循环
TypeSafe AI 与 LangChain 合作，把 Jev 放进 agent loop 做路由、护栏、批量决策与审计，称分类任务快 200 倍、成本低 400 倍。
this harness stack is actually f*cking insane TypeSafe AI + LangChain just shipped the piece that makes agents cheap enough to run across a whole company: jev sitting inside the langchain agent loop as middleware what every large team runs into: each branch in the loop costs another full LLM call. with 10 agents that's an invoice. with 10,000 it's the entire budget > routing old way: every task hits the frontier model new way: jev sends it to the cheapest model that can still close it. quick lookups stay light, architecture gets the heavy model > guardrails old way: the "is this command safe to run?" check lived inside the closed code of claude code, codex, cursor new way: AutoModeMiddleware(tools=["bash"]). a single line, works in any agent, stopped before the tool ever fires > decisions old way: one LLM call for every question new way: all questions about the same state go out in one request and come back in parallel > audit old way: "the model decided" new way: the answer, its probability and the confidence live in agent state > adoption nothing to rebuild. pip install langchain-typesafe and plug it into the agent you're already running TypeSafe claims up to 200x faster inference and 400x lower cost vs comparable LLMs on classification tasks. the LLM keeps doing the reasoning and writing. jev handles the thousands of tiny calls between them running in production today: browserbase agents at fractions of a cent, a live trading agent, email triage at volume whole loop mapped below: 7 agents, 4 jev gates per step go count the forks in your agent loop tonight
这条是引用着另一条写的：x.com/asteri_eth/status/2104901781725598103