LangChain

“#8 𝕏 Harrison Chase shared @ch3nweiii’s framing that many agent steps are yes/no calls rather than generation: Jev handles them as typed questions with calibrated probabilities, leaving harder work to the frontier model. LangChain’s “Building a Harness with Jev” explains where those calls fit within a harness.”

#8 𝕏 Harrison Chase shared @ch3nweiii’s framing that many agent steps are yes/no calls rather than generation: Jev handles them as typed questions with calibrated probabilities, leaving harder work to the frontier model. LangChain’s “Building a Harness with Jev” explains where those calls fit within a harness.

“In LangSmith evals, LangChain’s Jev assesses difficulty and correctness with separate typed answers in one pass on every trace.”

Harrison Chase commented that trajectory labeling involves several questions rather than a single pass/fail judgment. In LangSmith evals, LangChain’s Jev assesses difficulty and correctness with separate typed answers in one pass on every trace.

“Harrison Chase shared ModelRouterMiddleware, a LangChain project by @dbreunig, in the context of a DSPy and Jev model-router example.”

GenAI PM Daily - Perplexity Computer now generates interactive 3D learning visualizations October 04, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 12 insights for PM Builders from X and LinkedIn. Perplexity Computer now generates interactive 3D learning visualizations #3 𝕏 Harrison Chase shared ModelRouterMiddleware, a LangChain project by @dbreunig, in the context of a DSPy and Jev model-router example. Jev reads the first message to select a model for the entire run, with a custom hook allowing reselection after each tool result.

“Harrison Chase commented that agent teams want their own trace review UI, and that LangChain’s LangSmith Custom Apps lets users build one on their own data and ship it directly into the workspace.”

#6 𝕏 Harrison Chase commented that agent teams want their own trace review UI, and that LangChain’s LangSmith Custom Apps lets users build one on their own data and ship it directly into the workspace.

“Harrison Chase shared LangChain’s “How to Build a Custom Agent Harness,” describing it as a great blog by Sydney Runkle.”

#4 𝕏 Harrison Chase shared LangChain’s “How to Build a Custom Agent Harness,” describing it as a great blog by Sydney Runkle. The post quoted Garry Tan on systems of record versus domain-specific harnesses.

“Harrison Chase said agent workspaces should be durable, inspectable, and swappable, highlighting MongoDB support for LangChain’s Deep Agents virtual file system.”

Harrison Chase said agent workspaces should be durable, inspectable, and swappable, highlighting MongoDB support for LangChain’s Deep Agents virtual file system. Its BackendProtocol lets agent code use read, write, glob, and grep operations while teams choose their production storage layer.

“Harrison Chase said model labs will build harnesses for their own models while blocking access to rivals’ harnesses, making a lab-independent harness the only cross-model choice.”

#6 𝕏 Harrison Chase said model labs will build harnesses for their own models while blocking access to rivals’ harnesses, making a lab-independent harness the only cross-model choice. He expressed support for LangChain.

“AgentMail began after its founders tried to automate dropshipping by having LangChain agents sign up for Shopify store free trials; Gmail API-based and stitched-together email approaches did not provide agents with reliable programmatic email addresses.”

#8 ▶️ AgentMail Founder Adi Singh on Email for Agents SyntaxGTM Adi Singh explains that AgentMail provides API-first email inboxes for AI agents, including programmatic inbox creation, sending and receiving email, thread labeling, search, drafts, and webhook-triggered workflows. AgentMail began after its founders tried to automate dropshipping by having LangChain agents sign up for Shopify store free trials; Gmail API-based and stitched-together email approaches did not provide agents with reliable programmatic email addresses.

“Harrison Chase shared his perspective on the journey from early LangChain to managed agents, describing managed deepagents as one of the launches he had been more excited about in a while.”

#4 𝕏 Harrison Chase shared his perspective on the journey from early LangChain to managed agents, describing managed deepagents as one of the launches he had been more excited about in a while. He said managed agents could significantly improve how easy it is to run agents.

“Philipp Schmid : LangChain now lets you trace GoogleDeepMind Gemini Live speech-to-speech loops in real time—featuring speaker callback hooks, a color-coded live timeline (user in blue, Gemini in orange, tools in custom colors), and separate audio vs.”

#10 𝕏 Philipp Schmid : LangChain now lets you trace GoogleDeepMind Gemini Live speech-to-speech loops in real time—featuring speaker callback hooks, a color-coded live timeline (user in blue, Gemini in orange, tools in custom colors), and separate audio vs. #11 𝕏 Santiago launched a pay-as-you-go Apify Store skill that lets agents autonomously discover, trigger HTTP 402 payment prompts, authorize USDC on Base, and execute Actors end-to-end—bringing a full AI tools marketplace into agentic workflows.