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Powering the Agent Development Lifecycle. Makers of LangSmith and @LangChain_OSS and @LangChain_JS.

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We asked, you answered — our State of AI Agents Report is here! 🤖✨ We surveyed 1300+ industry professionals, from developers to business leaders, on how they're using AI agents today — and the results are in. What are the top use cases for agents? The biggest challenges when building agents? And who's finding success after deploying their agents to production? Read the full report ➡️ Here's 5 key insights in the thread below 🧵👇

We asked, you answered — our State of AI Agents Report is here! 🤖✨ We surveyed 1300+ industry professionals, from developers to business leaders, on how they're using AI agents today — and the results are in. What are the top use cases for agents? The biggest challenges when building agents? And who's finding success after deploying their agents to production? Read the full report ➡️ Here's 5 key insights in the thread below 🧵👇

103,549 views

🚀 LangSmith for Startups Spotlight: Cogent Security Cogent is building AI agents that protect the world's largest organizations from cyberattacks. One of the hardest problems in cybersecurity is going from finding a vulnerability to actually fixing it. Cogent is automating that entire process from end-to-end. Cogent is already working with dozens of Fortune 1000 and Global 2000 enterprise customers such as major universities, hospitality brands, and consumer retailers. Cogent uses LangSmith for production tracing and monitoring of our agents. Their team leverages execution traces for usage insight and use-case categorization, self-refinement loops to diagnose eval failures, and online evaluators to flag undesired behavior. Join their team if you want to build frontier AI for mission critical problems 🤝

🚀 LangSmith for Startups Spotlight: Cogent Security Cogent is building AI agents that protect the world's largest organizations from cyberattacks. One of the hardest problems in cybersecurity is going from finding a vulnerability to actually fixing it. Cogent is automating that entire process from end-to-end. Cogent is already working with dozens of Fortune 1000 and Global 2000 enterprise customers such as major universities, hospitality brands, and consumer retailers. Cogent uses LangSmith for production tracing and monitoring of our agents. Their team leverages execution traces for usage insight and use-case categorization, self-refinement loops to diagnose eval failures, and online evaluators to flag undesired behavior. Join their team if you want to build frontier AI for mission critical problems 🤝

18,513 views

Yesterday we launched the Agent Builder Template Library. Agent templates are more than a starting point. They’re ready to deploy and fully customizable by giving your agent feedback in plain language. 🧵 for highlights Try Agent Builder:

Yesterday we launched the Agent Builder Template Library. Agent templates are more than a starting point. They’re ready to deploy and fully customizable by giving your agent feedback in plain language. 🧵 for highlights Try Agent Builder:

13,699 views

LangSmith Traces 🤝 Server Logs View all user and system logs for a particular trace in a single window. Agentic observability just got even easier! ➡️ Docs:

LangSmith Traces 🤝 Server Logs View all user and system logs for a particular trace in a single window. Agentic observability just got even easier! ➡️ Docs:

10,974 views

✨ Klarna's AI Assistant, powered by LangGraph and LangSmith, handles customer support tasks for 85 million active users — reducing customer resolution time by 80% ✨ 🛍️ Klarna’s flagship AI Assistant is revolutionizing the shopping and payments experience. Built on LangGraph and powered by LangSmith, the AI Assistant handles tasks ranging from customer payments, to refunds, to other payment escalations. ‼️ In the past 9 months, they’ve reduced average customer query resolution time by 80%, enabling faster responses to user queries and saving analysts and engineers hours a week of investigation time. 🔄 They’ve also automated ~70% of repetitive support tasks, freeing up customer service agents to handle complex, high-value interactions

✨ Klarna's AI Assistant, powered by LangGraph and LangSmith, handles customer support tasks for 85 million active users — reducing customer resolution time by 80% ✨ 🛍️ Klarna’s flagship AI Assistant is revolutionizing the shopping and payments experience. Built on LangGraph and powered by LangSmith, the AI Assistant handles tasks ranging from customer payments, to refunds, to other payment escalations. ‼️ In the past 9 months, they’ve reduced average customer query resolution time by 80%, enabling faster responses to user queries and saving analysts and engineers hours a week of investigation time. 🔄 They’ve also automated ~70% of repetitive support tasks, freeing up customer service agents to handle complex, high-value interactions

10,852 views

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🚀 We just shipped a major update to LangSmith Agent Builder: • New agent chat: One always-available agent with access to all your workspace tools • Chat → Agent: Turn any conversation into a specialized agent with one click • File uploads: Attach files directly to Agent Builder • Tool registry: Add, authenticate, and manage your tools in one place Try it now: Learn more:

🚀 We just shipped a major update to LangSmith Agent Builder: • New agent chat: One always-available agent with access to all your workspace tools • Chat → Agent: Turn any conversation into a specialized agent with one click • File uploads: Attach files directly to Agent Builder • Tool registry: Add, authenticate, and manage your tools in one place Try it now: Learn more:

24,885,539 views • 3 months ago

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:

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:

9,397,938 views • 2 months ago

Just announced at Interrupt! SmithDB. Agent traces have outgrown the databases built to hold them. That’s why we built SmithDB, a purpose-built distributed database for agent observability. Read the announcement from Co-Founder Ankush Gola →

Just announced at Interrupt! SmithDB. Agent traces have outgrown the databases built to hold them. That’s why we built SmithDB, a purpose-built distributed database for agent observability. Read the announcement from Co-Founder Ankush Gola →

133,090 views • 21 days ago

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:

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:

148,506 views • 2 months ago

Build agents with LangChain + Browserbase. Give your Deep Agents search, fetch, and browser subagents to access the full web. All with full observability with the Browserbase dashboard.

Build agents with LangChain + Browserbase. Give your Deep Agents search, fetch, and browser subagents to access the full web. All with full observability with the Browserbase dashboard.

48,004 views • 1 month ago

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:

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:

108,918 views • 4 months ago

We raised $125M to build the platform for agent engineering. Thank you to our investors (IVP, Sequoia Capital, Benchmark, Amplify Partners, Sapphire Ventures, CapitalG, and more) for their belief in us, and to our customers like Replit ⠕, Clay, Vanta, Cloudflare, Rippling, Cisco, Workday, and many more for their trust. Read more here about what this means for the future of building agents:

We raised $125M to build the platform for agent engineering. Thank you to our investors (IVP, Sequoia Capital, Benchmark, Amplify Partners, Sapphire Ventures, CapitalG, and more) for their belief in us, and to our customers like Replit ⠕, Clay, Vanta, Cloudflare, Rippling, Cisco, Workday, and many more for their trust. Read more here about what this means for the future of building agents:

159,054 views • 7 months ago

🚀 Today we're launching LangSmith Sandboxes Agents get a lot more useful when they can run code: analyze data, call APIs, build entire applications. Sandboxes give them a safe place to do it with ephemeral, locked-down environments you control. Now in Private Preview. Learn more: Join the waitlist:

🚀 Today we're launching LangSmith Sandboxes Agents get a lot more useful when they can run code: analyze data, call APIs, build entire applications. Sandboxes give them a safe place to do it with ephemeral, locked-down environments you control. Now in Private Preview. Learn more: Join the waitlist:

46,430 views • 2 months ago

🚀 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 to the CLI, agents are able to do it all natively through the terminal, where they're most comfortable. Try out LangSmith Skills and the CLI with your own coding agents! ➡️ Skills: ➡️ CLI:

🚀 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 to the CLI, agents are able to do it all natively through the terminal, where they're most comfortable. Try out LangSmith Skills and the CLI with your own coding agents! ➡️ Skills: ➡️ CLI:

49,416 views • 3 months ago

Fleet now has shareable skills. Capture your team's domain knowledge once, attach it to any agent, and share it across your workspace. Create skills from a prompt or previous chat, write them manually, or use a template. Read more:

Fleet now has shareable skills. Capture your team's domain knowledge once, attach it to any agent, and share it across your workspace. Create skills from a prompt or previous chat, write them manually, or use a template. Read more:

36,366 views • 2 months ago

🚀Announcing LangGraph Studio: The first agent IDE LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith so you can collaborate with teammates to debug failure modes LangGraph Studio is available for free to all LangSmith users on any plan tier during its early development. Read more about it here: Watch a YouTube walkthrough: Try out LangGraph Studio for free here: Sign up for a LangSmith account:

🚀Announcing LangGraph Studio: The first agent IDE LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith so you can collaborate with teammates to debug failure modes LangGraph Studio is available for free to all LangSmith users on any plan tier during its early development. Read more about it here: Watch a YouTube walkthrough: Try out LangGraph Studio for free here: Sign up for a LangSmith account:

185,777 views • 1 year ago

🚀 LangSmith Agent Builder is GA – and we’re moving fast Agent Builder lets you build an agent with a simple prompt. But sometimes you want something that’s ready to go. Today we’re introducing the Agent Builder Template Library: ready-to-deploy agents built with the companies who know their domains best. Templates include: Tavily: competitor intelligence PagerDuty: on-call triage Box: document intake review +more Supports: Gmail, Google Calendar, Slack, Linear, Pylon, GitHub, LinkedIn, X, Tavily, Exa + any app with an MCP server 👉 Try Agent Builder: 📣 Read the announcement:

🚀 LangSmith Agent Builder is GA – and we’re moving fast Agent Builder lets you build an agent with a simple prompt. But sometimes you want something that’s ready to go. Today we’re introducing the Agent Builder Template Library: ready-to-deploy agents built with the companies who know their domains best. Templates include: Tavily: competitor intelligence PagerDuty: on-call triage Box: document intake review +more Supports: Gmail, Google Calendar, Slack, Linear, Pylon, GitHub, LinkedIn, X, Tavily, Exa + any app with an MCP server 👉 Try Agent Builder: 📣 Read the announcement:

46,023 views • 4 months ago

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

🚀 New LangChain Academy Course: Building Reliable Agents 🚀 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️

30,744 views • 3 months ago

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️

63,349 views • 8 months ago

🚀 Introducing LangGraph Cloud 🚀 LangGraph helps you build reliable agents that actually work. Today, we've launched LangGraph Cloud, our new infrastructure to run fault-tolerant LangGraph agents at scale. With LangGraph Cloud, you can: • Handle large workloads with horizontally-scaling servers, task queues, and built-in persistence • Debug agent failure modes and iterate quickly in a visual playground-like studio • Deploy in one-click and get integrated tracing & monitoring in LangSmith This builds on LangGraph v0.1, our latest stable release supporting diverse control flows - whether it's single- or multi-agent, hierarchal or sequential. You'll be able to create flexible agents with human-in-the-loop collaboration and first-class streaming support. We can't wait to see the agentic workflows you'll ship. LangGraph Cloud is available in closed beta today. 👉 Join the waitlist for LangGraph Cloud: ✍️ Read our blog post announcement: 📽️ Watch the video walkthrough: 🌀 Haven't tried LangGraph yet? Give it a spin here: 🙌 Check out the new LangGraph landing page:

🚀 Introducing LangGraph Cloud 🚀 LangGraph helps you build reliable agents that actually work. Today, we've launched LangGraph Cloud, our new infrastructure to run fault-tolerant LangGraph agents at scale. With LangGraph Cloud, you can: • Handle large workloads with horizontally-scaling servers, task queues, and built-in persistence • Debug agent failure modes and iterate quickly in a visual playground-like studio • Deploy in one-click and get integrated tracing & monitoring in LangSmith This builds on LangGraph v0.1, our latest stable release supporting diverse control flows - whether it's single- or multi-agent, hierarchal or sequential. You'll be able to create flexible agents with human-in-the-loop collaboration and first-class streaming support. We can't wait to see the agentic workflows you'll ship. LangGraph Cloud is available in closed beta today. 👉 Join the waitlist for LangGraph Cloud: ✍️ Read our blog post announcement: 📽️ Watch the video walkthrough: 🌀 Haven't tried LangGraph yet? Give it a spin here: 🙌 Check out the new LangGraph landing page:

134,051 views • 1 year ago

🔗 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 ➡️

🔗 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 ➡️

41,016 views • 5 months ago

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - 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 and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - 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 and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

50,221 views • 7 months ago

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

61,205 views • 9 months ago

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

105,415 views • 1 year ago

🪄 LangChain State of AI 2024 What LLMs are the most widely used today? What metrics are commonly used for evals? Are developers finding success in building agents? Our State of AI 2024 report shows where the AI ecosystem is headed, based on data from LangSmith. Key 5 insights in the thread 🧵👇 Full report:

🪄 LangChain State of AI 2024 What LLMs are the most widely used today? What metrics are commonly used for evals? Are developers finding success in building agents? Our State of AI 2024 report shows where the AI ecosystem is headed, based on data from LangSmith. Key 5 insights in the thread 🧵👇 Full report:

89,064 views • 1 year ago