chairman and CEO Arvind Krishna announced Gerstner’s death in an email sent Sunday to its employees, but didn’t provide a cause of death.Gerstner’s nine-year tenure as chairman and CEO of the company known as “Big Blue” is often used as a case study in corporate leadership.On April Fool’s Day, 1993, he became the first outsider to run IBM, which was facing a choice of bankruptcy or dismemberment after a period when it had been the undisputed leader in personal computers and mainframes. He pivoted the Armon ...

Hi there, this is Christian from Lchain. In my last video, we looked at how summarization middleware keeps your agents memory compact by rewriting the entire conversation history. But what if the problem isn't the conversation, it's the tools.Because here's the truth. Modern agents don't just talk. They call tools over and over again.And those tool results can absolutely explode your conductor window. Unlike user messages, tool outputs can be huge. I mean, we're talking about 20 pages of search results, a m ...

Hi, this is Christian from LChain. Have you ever built an agent that just goes nuts with your API calls. Tools can give an agent incredible power, but can also cost you a lot of money to run.In this video, I will show you how you could keep your agent under control without any hard-coded guardrails within your system prompt. Today, we're taking a look at the tool called middleware within LChain. It's a clean declarative way to set credit limits, rate limits, or usage caps on any tools your agent uses.Think ...

Hey folks, it's Sydney from the Python open source team at Langchain, and I'm super excited to bring you our next edition of our middleware series. So, model outages can be incredibly unpredictable, but luckily with Langchain's new model fallback middleware, the reliability of your application doesn't have to be. Whatever the reason may be that your model calls are no longer working, that could be a provider outage or an API quota exhausted, lingchain's model fallback middleware can help you fall back to a ...

Hi there, this is Christian from LChain. Here's a scenario. We want a customer support agent that handles routine questions.But if the user keeps going back and forth with it, maybe asking the same thing in three different ways, that's usually a sign that something is not working. Either the agent isn't resolving the issue or the user's drifting into a too long unproductive conversation that honestly the bot was never meant to handle in the first place. In both cases, we want the agent to stop trying to be ...

Core Concept - Introduces the concept of "human-in-the-loop" middleware for Langchain agents, allowing human review and intervention in agent workflows [5][18] - Explains the agent's reasoning loop: reason, act, observe, and how human intervention fits into this loop [3][5] - Highlights the three decision types for human reviewers: approve, edit, and reject, and how these decisions guide the agent's subsequent actions [7] Technical Implementation - Demonstrates the integration of a Langchain agent with human-in-the-loop middleware in a Nextjs application for sending emails [2][17] - Emphasizes the importance of a checkpointer (using Redis database) to store the agent's state and enable resuming the workflow after human intervention [13][14] - Describes how the middleware intercepts tool calls (e g, sending emails) and pauses the agent's execution, awaiting human input [5][6] Benefits and Use Cases - Positions human-in-the-loop as a way to combine agent autonomy with human oversight, especially for actions with risk or requiring judgment [18][19] - Suggests use cases such as sending emails, updating records, or writing to external systems, where human review is valuable [19] - Underscores the flexibility of the middleware, allowing customization of interruption logic based on tool name, arguments, or runtime context [19][20] Practical Example - Provides a practical example of using the middleware to allow a human to revise an email drafted by the agent before it is sent [2][16] - Showcases how to reject a proposed action and provide feedback to the agent, influencing its subsequent behavior [16] - Mentions a publicly available repository (github com/christian broman/lunghat) for users to experiment with the human-in-the-loop concept [20]

And so you have this iterative process of creating the right prompt and shaping the right guard rails and other code in order to get it to be like really useful in those situations. Open source has been a huge part of lang chain from ever since we got started. Obviously it started as an open source package and it's evolved a lot over the years.We now have Typescript packages. We now have lang chain and langraph. And so you know as we release 1.0% know of these packages.It's a huge moment for us as a company ...

Market Overview - Virtuals 市值达到 749 million 美元 [1] - 300 thousand 个积分产生 50 美元的奖励 [1] - Solana agent 中间件基础设施成本低于 50 thousand 美元 [1] Technology & Infrastructure - Solana agent 中间件服务于 338 亿 (33.8 billion) 美元的 TVL (总锁定价值) 生态系统 [1] - 每个 AI agent 都需要相应的 rails (基础设施) [1] Platform Performance - 平台活动处于 beta 阶段的低谷 [1]