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🚀 Announcing LangSmith Skills + CLI 🚀 Agent improvements are increasingly driven by coding agents themselves. We're releasing LangSmith Skills alongside the LangSmith CLI to make coding agents experts at the agent engineering lifecycle. LangSmith Skills enable agents to debug traces, create datasets, and run experiments - and thanks...

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LangChain

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49,662 görüntüleme • 7 ay önce •via X (Twitter)

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WildPinesAI profil fotoğrafı
WildPinesAI7 ay önce

@hwchase17 piping LangSmith traces directly into your coding agent so it debugs your other agents — agents improving agents is a literal workflow now. CLI-first was the right call

Sadi Moodi profil fotoğrafı
Sadi Moodi7 ay önce

This is the future of agent engineering. Key insight: when agents can debug their own traces via CLI, you get compounding improvements. My checklist for agentic workflows: 1) instrument every step with trace IDs, 2) build feedback loops where agents learn from failed runs, 3) keep agents in terminal where they're most productive. The 50% VRAM reduction from Unsloth + LangSmith = serious edge deployment potential.

Samuel Ekpe profil fotoğrafı
Samuel Ekpe7 ay önce

Nice

danfiru profil fotoğrafı
danfiru7 ay önce

the toolchain is eating itself. agents improving agents. the question is whether this feedback loop converges on better software or just more software. best stacks will be the ones that know when to stop generating.

Verivus Open Source profil fotoğrafı
Verivus Open Source7 ay önce

check out to get sqry, ships as an MCP server (33 tools). Claude Code, Codex, Gemini can query the actual call graph instead of guessing. make @LangChain smarter.

Prakhar Khatri profil fotoğrafı
Prakhar Khatri7 ay önce

Gonna try it Looks good

lakshya profil fotoğrafı
lakshya7 ay önce

Thank you! I was making do with the locally hosted MCP server and it was quite finicky.

anil kalm profil fotoğrafı
anil kalm7 ay önce

Agents improving agents via traces + CLI feels like the toolchain finally eating itself in the best way. One thing I'm wondering: how are you all thinking about stopping conditions or 'convergence' in these self-improvement loops so it doesn't just keep generating more code forever?

Vikas Kumar | Agent Engineer profil fotoğrafı
Vikas Kumar | Agent Engineer7 ay önce

We're getting closer to agents running the full engineering loop: build → debug → eval → improve.

Chris Ruppelt profil fotoğrafı
Chris Ruppelt7 ay önce

Awesome, would be cool to get some example usages. How do i get claude code running to fix a specific langgraph call given the langsmith run/trace id?

VibeCodes profil fotoğrafı
VibeCodes7 ay önce

Agents improving their own traces via CLI is a huge step. Self-improving workflows > one-shot execution. We're building on this vision at @getvibecodes — AI agents as persistent team members across the full dev lifecycle. LangSmith Skills makes the ecosystem stronger for everyone.

tommyt profil fotoğrafı
tommyt7 ay önce

The AI slop replies on langchain X posts are unbearable

Benzer Videolar

Today, we’re shipping new ways to observe, analyze, and debug agents with LangSmith: • Polly: an AI assistant for AI engineering that helps you understand traces, threads, and improve prompts • LangSmith Fetch: a CLI for pulling trace & thread data straight into your terminal or coding agent Agents are running longer and getting more complex, which demands new debugging workflows beyond simple LLM apps. We wrote a blog on the trends behind this shift— and why tools like Polly and LangSmith Fetch are needed. Shipping reliable agents requires full visibility into agent behavior, with tooling that helps you reason over that data in the UI, the terminal, or alongside coding agents. 📔Learn more about Polly: 📔Learn more about LangSmith Fetch: 📔How to observe deep agents: 📽️ Polly video tutorial: 📽️ LangSmith Fetch video tutorial:
7:10

Today, we’re shipping new ways to observe, analyze, and debug agents with LangSmith: • Polly: an AI assistant for AI engineering that helps you understand traces, threads, and improve prompts • LangSmith Fetch: a CLI for pulling trace & thread data straight into your terminal or coding agent Agents are running longer and getting more complex, which demands new debugging workflows beyond simple LLM apps. We wrote a blog on the trends behind this shift— and why tools like Polly and LangSmith Fetch are needed. Shipping reliable agents requires full visibility into agent behavior, with tooling that helps you reason over that data in the UI, the terminal, or alongside coding agents. 📔Learn more about Polly: 📔Learn more about LangSmith Fetch: 📔How to observe deep agents: 📽️ Polly video tutorial: 📽️ LangSmith Fetch video tutorial:

LangChain

31,193 görüntüleme • 10 ay önce

UI Skills are now easier for agents. Introducing UI Skills CLI. ask Codex, Cursor, Claude Code, or any agent to use the CLI, like: "use `npx ui-skills start` to fix the motion in this dialog" it finds the best design-engineering skill for the job and uses it as context
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UI Skills are now easier for agents. Introducing UI Skills CLI. ask Codex, Cursor, Claude Code, or any agent to use the CLI, like: "use `npx ui-skills start` to fix the motion in this dialog" it finds the best design-engineering skill for the job and uses it as context

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Give Claude Code, Codex, OpenClaw, Cline access to the entire web from your terminal. Introducing TinyFish CLI + Skills. 15+ agent workflows ready to use. CLI runs browser automations from bash for max token efficiency. Skills tell agents how to use that CLI. 500 free steps.
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Give Claude Code, Codex, OpenClaw, Cline access to the entire web from your terminal. Introducing TinyFish CLI + Skills. 15+ agent workflows ready to use. CLI runs browser automations from bash for max token efficiency. Skills tell agents how to use that CLI. 500 free steps.

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Introducing LangSmith Fleet. Agents for every team. → Build agents with natural language → Share and control who can edit, run, or clone each agent → Manage authentication with agent identity → Approve actions with human-in-the-loop → Track and audit actions with tracing in LangSmith Observability Try Fleet:
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Introducing LangSmith Fleet. Agents for every team. → Build agents with natural language → Share and control who can edit, run, or clone each agent → Manage authentication with agent identity → Approve actions with human-in-the-loop → Track and audit actions with tracing in LangSmith Observability Try Fleet:

LangChain

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Introducing mem0 CLI 🚀 Give your agents memory from the terminal - add, search, and retrieve memory - remember agents + developers's preference - agent-first, JSON schema-driven We built mem0 CLI to help agents remember Get started npm install -g @mem0/cli Open source 👇
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Introducing mem0 CLI 🚀 Give your agents memory from the terminal - add, search, and retrieve memory - remember agents + developers's preference - agent-first, JSON schema-driven We built mem0 CLI to help agents remember Get started npm install -g @mem0/cli Open source 👇

Taranjeet

20,567 görüntüleme • 6 ay önce

🔗 Announcing LangChain OSS Skills LangChain has the most popular frameworks for building AI agents — and now your coding agent can be an expert in it. We're excited to release the first iteration of LangChain OSS Skills, giving your agent expertise in our open source frameworks. The skills include guidance on how to use langchain, langgraph, and deepagents to effectively build agents. ➡️ Install our OSS skills for your coding agent here: ➡️ Read more:
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🔗 Announcing LangChain OSS Skills LangChain has the most popular frameworks for building AI agents — and now your coding agent can be an expert in it. We're excited to release the first iteration of LangChain OSS Skills, giving your agent expertise in our open source frameworks. The skills include guidance on how to use langchain, langgraph, and deepagents to effectively build agents. ➡️ Install our OSS skills for your coding agent here: ➡️ Read more:

LangChain OSS

65,840 görüntüleme • 7 ay önce

Introducing: Superset CLI 🎮 Drive Superset from within the terminal, or sit back and let your agents do it 🏖️ Create workspaces, spin up agents, edit tasks, etc. All through the terminal, updates the app in real-time. Works with automation and remote workspaces. Teach it to your agents with skills: npx skills add Available now: brew install superset-sh/tap/superset
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Introducing: Superset CLI 🎮 Drive Superset from within the terminal, or sit back and let your agents do it 🏖️ Create workspaces, spin up agents, edit tasks, etc. All through the terminal, updates the app in real-time. Works with automation and remote workspaces. Teach it to your agents with skills: npx skills add Available now: brew install superset-sh/tap/superset

Superset

19,847 görüntüleme • 5 ay önce

Polly is our AI assistant built directly into LangSmith to help you debug, analyze, and improve your agents — now generally available. Now, Polly lives on every page of LangSmith, remembers your full session as you navigate, and can take action to update prompts, compare experiments, write evaluators, and more. Read the blog: See the docs: Try Polly in LangSmith:
2:05

Polly is our AI assistant built directly into LangSmith to help you debug, analyze, and improve your agents — now generally available. Now, Polly lives on every page of LangSmith, remembers your full session as you navigate, and can take action to update prompts, compare experiments, write evaluators, and more. Read the blog: See the docs: Try Polly in LangSmith:

LangChain

12,595 görüntüleme • 7 ay önce

Introducing the Firecrawl Skill + CLI for Agents 🔥 Agents like Claude Code, Codex, and OpenCode need live quality context from the web. The CLI pulls web content to local files with bash-powered search for the highest token efficiency. $ npx skills add firecrawl/cli
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Introducing the Firecrawl Skill + CLI for Agents 🔥 Agents like Claude Code, Codex, and OpenCode need live quality context from the web. The CLI pulls web content to local files with bash-powered search for the highest token efficiency. $ npx skills add firecrawl/cli

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87,256 görüntüleme • 9 ay önce

Meet AMD Skills: A growing catalog of agent skills that helps AI coding assistants work more effectively with AMD hardware and software. Compatible with coding agents including Cursor, Claude Code, OpenAI Codex, and Gemini CLI, AMD Skills provides the knowledge, scripts, and best practices developers need to build faster on AMD. Why try it? → Install in seconds with the Skills CLI → Learn AMD best practices and workflows → Build AI applications from client to cloud → Pick only the skills you need Get started: npx skills add amd/skills Explore the project, install a skill, and let us know what you'd like to see next:
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Meet AMD Skills: A growing catalog of agent skills that helps AI coding assistants work more effectively with AMD hardware and software. Compatible with coding agents including Cursor, Claude Code, OpenAI Codex, and Gemini CLI, AMD Skills provides the knowledge, scripts, and best practices developers need to build faster on AMD. Why try it? → Install in seconds with the Skills CLI → Learn AMD best practices and workflows → Build AI applications from client to cloud → Pick only the skills you need Get started: npx skills add amd/skills Explore the project, install a skill, and let us know what you'd like to see next:

AI at AMD

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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail
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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

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33,192 görüntüleme • 1 yıl önce

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Introducing Duet CLI. Now coding agents like Claude Code and Codex can call Duet (our agent-building agent) directly from the terminal to build and modify Decagon agents. Duet handles work within Decagon and returns the result while your coding agent stays focused on the broader task.

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LangSmith Agent Builder is generally available 🎉 It’s surprisingly easy to build agents now. Even a VC can do it…👇 Try it free: Read the announcement:
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LangSmith Agent Builder is generally available 🎉 It’s surprisingly easy to build agents now. Even a VC can do it…👇 Try it free: Read the announcement:

LangChain

111,717 görüntüleme • 9 ay önce

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️
2:25

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

LangChain

41,233 görüntüleme • 10 ay önce

The Perplexity CLI is now available, giving coding agents the ability to search the web. Copy this to your agent to get set up: "Read: and install this skill."
0:20

The Perplexity CLI is now available, giving coding agents the ability to search the web. Copy this to your agent to get set up: "Read: and install this skill."

Perplexity Developers

162,032 görüntüleme • 3 ay önce

AI agents can now autonomously discover and install Marketplace integrations using the Vercel CLI. ▲ ~/ 𝚗𝚙𝚡 𝚜𝚔𝚒𝚕𝚕𝚜 𝚊𝚍𝚍 𝚟𝚎𝚛𝚌𝚎𝚕/𝚟𝚎𝚛𝚌𝚎𝚕 --𝚜𝚔𝚒𝚕𝚕 𝚟𝚎𝚛𝚌𝚎𝚕-𝚌𝚕𝚒 Install the CLI skill and ask your coding agent to integrate a Vercel Marketplace product.
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AI agents can now autonomously discover and install Marketplace integrations using the Vercel CLI. ▲ ~/ 𝚗𝚙𝚡 𝚜𝚔𝚒𝚕𝚕𝚜 𝚊𝚍𝚍 𝚟𝚎𝚛𝚌𝚎𝚕/𝚟𝚎𝚛𝚌𝚎𝚕 --𝚜𝚔𝚒𝚕𝚕 𝚟𝚎𝚛𝚌𝚎𝚕-𝚌𝚕𝚒 Install the CLI skill and ask your coding agent to integrate a Vercel Marketplace product.

Vercel Developers

210,635 görüntüleme • 7 ay önce

Introducing LangSmith Fleet: an enterprise workspace for creating, using, and managing your fleet of agents. Fleet agents have their own memory, access to a collection of tools and skills, and can be exposed through the communication channels your team uses every day. Fleet includes: → Agent identity and credential management with “Claws” and “Assistants” → Sharing and permissions to control who can run, clone, and edit (just like Google Docs) → Custom Slack bots so each agent has its own identity in Slack Try Fleet: Read the announcement:
1:57

Introducing LangSmith Fleet: an enterprise workspace for creating, using, and managing your fleet of agents. Fleet agents have their own memory, access to a collection of tools and skills, and can be exposed through the communication channels your team uses every day. Fleet includes: → Agent identity and credential management with “Claws” and “Assistants” → Sharing and permissions to control who can run, clone, and edit (just like Google Docs) → Custom Slack bots so each agent has its own identity in Slack Try Fleet: Read the announcement:

LangChain

149,147 görüntüleme • 7 ay önce

📊 Learn how to observe & evaluate agents on LangChain Academy 📊 Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling agents, and testing agents becomes even more complex than traditional software testing 😵‍💫 To address this challenge, LangSmith provides a comprehensive platform for agent engineering that helps teams use live production data for continuous testing and improvement 🚀 In our quickstart course, LangSmith Essentials, you’ll learn how to observe, evaluate, and deploy your agent in less than 30 minutes. Enroll for free ➡️
1:44

📊 Learn how to observe & evaluate agents on LangChain Academy 📊 Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling agents, and testing agents becomes even more complex than traditional software testing 😵‍💫 To address this challenge, LangSmith provides a comprehensive platform for agent engineering that helps teams use live production data for continuous testing and improvement 🚀 In our quickstart course, LangSmith Essentials, you’ll learn how to observe, evaluate, and deploy your agent in less than 30 minutes. Enroll for free ➡️

LangChain

13,518 görüntüleme • 9 ay önce

Blackbox Agent through the CLI got new some SKILLs design make sure to use `/skills` to give the agent more specialization on the tasks you want
0:47

Blackbox Agent through the CLI got new some SKILLs design make sure to use `/skills` to give the agent more specialization on the tasks you want

BLACKBOX AI

56,614 görüntüleme • 10 ay önce

Today we're open sourcing a technical preview of the GitHub Copilot CLI SDK. Build agents with custom tools in Go, Python, TypeScript, and C#. Built on the same agent loop that powers the Copilot CLI and GitHub Coding Agent. Supports BYOK, and any model. Here is the Copilot CLI driving Excel:
0:29

Today we're open sourcing a technical preview of the GitHub Copilot CLI SDK. Build agents with custom tools in Go, Python, TypeScript, and C#. Built on the same agent loop that powers the Copilot CLI and GitHub Coding Agent. Supports BYOK, and any model. Here is the Copilot CLI driving Excel:

Evan Boyle

184,599 görüntüleme • 9 ay önce