Continual Learning in AI Agents - The industry recognizes the gap between AI agents and human learning capabilities, emphasizing the need for agents to continually learn and improve over time [1] - The industry is exploring different methods for AI systems to learn, including weight updates and learning in context using large language models (LLMs) [2] - Reflection over trajectories is emerging as a key theme, allowing agents to update memories, core instructions, and learn new skills [3][4][5] Skill Learning and Implementation - Skill learning involves reflecting over trajectories to learn skills, exemplified by the skill creator skill adapted from Anthropic [8][9] - Deep agent CLI allows specifying environment variables for logging traces, which is useful for reflection [10][11] - The industry is using Langsmith Fetch to grab recent threads from deep agents for reflection and persistent skill creation [12][13] - A practical example demonstrates how an agent can read a JSON file, reflect on its contents, and create a new deep agent skill, showcasing the utility of continual learning [15][16][17] Benefits and Future Directions - Skill learning enables agents to encapsulate standard operating procedures, such as grabbing Langsmith traces, for repeated use [19][20] - Continual learning loop involves agents reflecting on past trajectories to learn facts, memories, skills, and improve instructions [21][22]

Are you curious about what cloud code is doing behind the scenes. Or do you want observability in the critical workflows that you've set up with claude code. Hey, I'm Tanish from Langchain and we built a claude code to LinkSmith integration so that you can see each step that cla takes whether that be an LLM call or tool calls.Um it's pretty fascinating to see the entire trace. So I want to show you what this looks like. Um uh I have uh a project here.It's a very very very simple uh agent that I build with u ...

Memory Updating Mechanisms for Agents - Explicit memory updating involves directly instructing the agent to remember specific information, similar to how cloud code functions [2][5][6][29] - Implicit memory updating occurs through the agent learning from natural interactions with users, revealing preferences without explicit instructions [7][19][29] Deep Agent CLI and Memory Management - Deep agents have a configuration home directory with an `agent MD` file that stores global memory, similar to Claude's `cloud MD` [3][4][6] - The `agent MD` files are automatically loaded into the system prompt of deep agents, ensuring consistent memory access [6] - Deep agent CLI allows adding information to global memory using natural language commands, updating the `agent MD` file [5] Implicit Memory Updating and Reflection - Agents can reflect on past interactions (sessions or trajectories) to generate higher-level insights and update their memory [8][9][10][28] - Reflection involves summarizing session logs (diaries) and using these summaries to refine and update the agent's memory [11][12] - Accessing session logs is crucial for implicit memory updating; Langsmith can be used to store and manage deep agent traces [13][14][15] Practical Implementation and Workflow - A utility can be used to programmatically access threads and traces from Langsmith projects [21] - The deep agent can be instructed to read interaction threads, identify user preferences, and update global memory accordingly [24][25] - Reflecting on historical threads allows the agent to distill implicit preferences and add them to its global memory, improving future interactions [26][27][28]

I’m excited to announce the release of our latest LangChain Academy foundations course, Introduction to LangChain in Python. We’ve entered a new era of AI, one where our apps don’t just respond, they think, plan, and act autonomously. Today, we're building agents – AI systems that can reason and interact with their environments to get real work done.Imagine a team of assistants that can summarize your inbox, schedule meetings, and perform market research 24/7. In this course, you'll learn to build deploymen ...

Key Functionality & Benefits - Langchen introduces native provider tools from OpenAI and Entropic, enabling optimized model usage [1] - The new Langchain provider packages simplify agent building by eliminating manual JSON schema handling [2] - Cloud can dynamically discover and load tools on demand, facilitating operations on platforms like Cloudflare [3] - Native provider tools unlock real-world multimodal agentic applications [4] - The MCP tool set and tool search provider tool are more efficient than locally spinning up MCP servers [15] - The new tools API eliminates the need for handcrafting schemas and gluing adapters [17] Cloudflare MCP Agent Example - An agent connects to Cloudflare's MCP servers, granting access to the entire platform via a chat interface [5] - The agent can fetch DNS logs, run queries, and answer questions about a user's Cloudflare account [3] - The agent can access Cloudflare browsers to fetch websites and convert them to markdown or screenshots [7][14] - The MCP tool set tool from the entropic provider package allows defining configuration for every MCP server [10] - The tool search tool defers loading of MCP server tools, saving context window space [11][12]

Hey, this is Lance. Recently put out this blog post called debugging deep agents with lang. And the big idea here was connecting lang as a system of record for your traces with code agents like deep agents, but it could be other code agents like clock code to create kind of an iterative feedback loop.So you're having a code agent produce some langraph code that's being run. Traces are going to lang. And there's a way for the code agents to pull traces back, reflect on them, and update your lane share langra ...

Hi, this is Christian from Lchain. Many LM providers now ship their own native tools. Not just generic function calls, but tools the model specifically trained and tuned to work with like entropics computer use, web search, bash or memory tools.With the latest version of lung chains, entropic and open air provider packages, we expose these simple provider tools so that you can call these model optimized tools seamlessly and type safe with your agent without hand rolling any JSON schemas or glue code. In thi ...

Explore the unique challenges of observing and evaluating Deep Agents in production. Deep Agents represent a shift in how AI systems operate – unlike simple chatbots or basic RAG applications, these agents run for extended periods, execute multiple sub-tasks, and make complex decisions autonomously. In this session, we'll dive into practical approaches for gaining visibility into Deep Agent behavior and measuring their effectiveness using LangSmith. Learn more about Deep Agents here: https://blog.langchain. ...

Hey, I'm Tanish from Langchain. Today I'm going to go through how to use Open Router's new broadcast feature with Langchain to send traces to Langmith. The cool thing about broadcast is it stores destination information, which in this case is Langmith server side.So the only thing that you need to worry about in your code is your open router API key. Let's go through how to set this up. So let me walk you through a quick code snippet.This uses lang chain's init chat model in order to initialize a model and ...

Today we're excited to launch Langmith fetch. Langmith fetch is a command line util for pulling data from Langmith into your local file system. We think this is super useful for giving coding agents like cloud code and codecs or deep agent CLI the ability to pull down Langmith data.Why is this useful. Well, a lot of agents that are getting traced to Linksmith are starting to become longer and longer and have many tool calls and larger prompts. And analyzing and debugging all of that data by hand and by eye ...