LangChain came up in “⚡️ Ship AI recap: Agents, Workflows, and Python — w/ Vercel CTO Malte Ubl” from Latent Space: The AI Engineer Podcast.
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So that uh Yeah, it was was great. Okay, awesome. So we can come back to a workflow anytime you want. Uh But I just wanna keep keep moving on on like all the stuff you had announced. Uh we should probably also just touch on the ISD. I know you're not like as closely involved to…
LangChain came up in “Building Production-Ready AI Agents with Pydantic AI” from AI Engineering Podcast.
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only three that have really come to any prominence this year are us Google and OpenAI and we're we're Probably the biggest differentiator is the there is a there is the this a this modern definition of an Agent, uh I say modern, as in this year, maybe very end of last year, that…
LangChain came up in “Harness Engineering 101” from The AI Daily Brief: Artificial Intelligence News and Analysis.
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Harness engineering is the subset of context engineering which primarily involves leveraging harness configuration points to carefully manage the context window of coding agents. It answers how do we give our coding agents new capabilities? How do we teach it things about our co…
LangChain came up in “E178: Building Safer AI Agents with Portia AI” from Open Source Startup Podcast.
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the just in time authorization behavior that we wanted because most Agent frameworks build that stage into the point after the agent has almost completed Whereas what we needed was the ability to really get in there into kind of like the internal gubbins of the system and it jus…
LangChain came up in “402 Payment Required: a New Way for AI Agents to Pay, with Nemil Dalal, Dev Platform Lead @ Coinbase” from "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis.
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Build the future of multi-agent software with agency, AGNT. The agency is an open source collective building the Internet of Windows. agents. It's a collaboration layer where AI agents can discover, connect, and work across frameworks. For developers, this means standardized age…
LangChain came up in “985: The Four Types of Memory Every AI Agent Needs, with Richmond Alake” from Super Data Science: ML & AI Podcast with Jon Krohn.
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in the space of actually providing some tools, some open source tools that some of our So one of them is agent spec, which is this solution to actually making our agents portable. across different frameworks. Because one thing we saw was this sprawl of people building their agen…
LangChain came up in “#157 Architecting the Intelligent Enterprise - an intro to AI in the context of EA” from XTraw AI: Machine Learning and AI Applications.
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This sounds like an architectural necessity for enterprise Gen AI. It is. Instead of relying solely on the vast general knowledge baked into the LLM, RA instructs the LLM to generate answers based only on relevant data retrieved from internal, secure, and up to date sources. Thi…
LangChain came up in “Running AI MCP Tools on Kubernetes with kagent” from Agentic DevOps : AI Engineering for Infrastructure.
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necessarily obviously to to us maybe. But basically I think that's actually a big problem. When I talk to people at KubeCon, one of the biggest takeaways for me was people don't know how to run these things. Like they they build something on their laptop, they have a little bit…
LangChain came up in “Along The Edge e3: Breaking AI Agents: From Jailbreaks to MCP Exploits with Javi Rivera” from Along The Edge Podcast: Breaking, Defending, and Understanding Agentic AI.
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The uh like in malicious behavior, like in intentionally deploying M C P servers are bad. for you. Now again for the other side there's not yeah there's nothing that's actually going after the They're doing it internally, maybe, but that's an that are you w are you willing as a…
LangChain came up in “The rise of AI agents” from Gradient Dissent: Conversations on AI.
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model that it will be more expensive and a little a little more slow. Now um I want to talk a little bit about your your architecture. I think you sort of visibly took um lang chain kind of out of your out of your stack. Can you talk about why you why you did that? Yeah, sure. T…
LangChain came up in “Software Engineering in the Age of Coding Agents: Testing, Evals, and Shipping Safely at Scale” from MLOps.community.
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Like, huh, I wonder if it's not working because this problem is too hard. hard or if it and it's not capable or it just like got stuck on one of the loops and it wasn't able to complete it. So I think there's a lot of that investigation work too that becomes a little bit of a nu…
LangChain came up in “Semantic Operators Meet Dataframes: Building Context for Agents with FENIC” from Data Engineering Podcast.
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serve the context for your agent. So it doesn't replace the agetic frameworks, right? be thought as something that can enhance your like agent framework that you use, like Pythonic AI or like Lang Graph or anything else. Built the state management, the context management using F…