Langchain agents. Learn to build smarter, adaptive systems today.

Langchain agents. This post outlines how to build 3 reflection techniques using LangGraph, including implementations of Reflexion and Language Agent Tree Search. This is usually powered by a language model, a prompt, and an output parser. Are AI agents being used in production? What's the biggest challenge to deploying agents - cost, quality, skill, or latency? Get insights on AI agent adoption and sentiment for devs and enterprises today. Functions Agent Types This categorizes all the available agents along a few dimensions. List [~langchain_core. We hope to continue developing different toolkits that can enable agents to do amazing feats. Agents Chains are great when we know the specific sequence of tool usage needed for any user input. abc import Sequence from typing import Any, Literal, Union from langchain_core. Classes Jun 12, 2024 · Exploring LangChain Agents with Memory: Basic Concepts and Hands-On Code Introduction LangChain is a framework for developing applications powered by large language models (LLMs). Deprecated since version 0. In this tutorial, we'll build a customer support bot that helps users navigate a digital music store. That's where Agents come in! LangChain comes with a number of built-in agents that are optimized for different use Oct 29, 2024 · Build dynamic conversational agents with custom tools to enhance user interactions, delivering personalized, context-driven responses. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. Find answers to specific questions, examples, and tutorials for each component. So, are you ready to let your language model take the Aug 3, 2023 · At LangChain, we have had components for these trends from the very beginning. g. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well. We are announcing: * Agent Protocol: a common interface for agent This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly. Agents Agents can be thought of as the chain responsible for deciding what step to take next. Agents LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. Emphasize is on how to use LangChain /Agents capabilities to monitor an API’s health and send email alerts if the service is down. See how to use it on your desktop today. Reflection Agents Reflection is a prompting strategy used to improve the quality and success rate of agents and similar AI systems. Everyone seems to have a slightly different definition of what an AI agent is. Setup: LangSmith By definition, agents take a self-determined, input-dependent Jun 28, 2024 · “What is an agent?” I get asked this question almost daily. Intermediate agent actions and tool output messages will be passed in here. LangChain comes with a number of built-in agents that are optimized for different use cases. tools. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. Learn to build smarter, adaptive systems today. This is driven by a LLMChain. Dec 31, 2024 · 2024 was the year that agents started to work in production. Class hierarchy: Jan 3, 2025 · Langchain LangChain is a robust framework for building applications powered by large language models (LLMs). Rather than forcing users into new chat windows, these agents help save your attention for when it matters most. For details, refer to the LangGraph documentation as well as guides for Aug 1, 2024 · LangGraph Studio provides a specialized agent IDE for visualizing, interacting with, and debugging complex agentic applications. Feb 15, 2025 · Agents in LangChain are advanced components that enable AI models to decide when and how to use tools dynamically. The main thing this affects is the prompting strategy used. Today, we’re excited to announce Command: a new tool in langgraph to more easily facilitate multi-actor (or multi-agent) communication agents # Agent is a class that uses an LLM to choose a sequence of actions to take. Retrieval Augmented Generation (RAG) Part 2: Build a RAG application that incorporates a memory of its user interactions and multi-step retrieval. Jan 14, 2025 · Over the past six months, we've been exploring a different approach at LangChain: agents that respond to ambient signals and demand user input only when they detect important opportunities or require feedback. See the previous post on planning here, and the previous posts on UX here, here, and here. Toolkits are supported Feb 6, 2025 · Agents are autonomous systems within LangChain that take actions based on input data. Apr 11, 2024 · Quickstart To best understand the agent framework, let's build an agent that has two tools: one to look things up online, and one to look up specific data that we've loaded into a index. In it, we leverage a time-weighted Memory object backed by a LangChain retriever. Not the wide-ranging, fully autonomous agents that people imagined with AutoGPT. An agent is a custom By themselves, language models can't take actions - they just output text. You can use an agent with a different type of model than it is intended for, but it likely won't produce AgentExecutor # class langchain. To address these issues and facilitate communication with external applications, we introduce the concept of an Agent as a processor. When the agent reaches a stopping condition, it returns a final return value. To read more about how the interrupt function works, see the LangGraph documentation: conceptual guide how-to guide (TypeScript docs coming soon, but the concepts & implementation are the same). agents. Productionization To use the Agent Inbox, you'll have to use the interrupt function, instead of raising a NodeInterrupt exception in your codebase. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. While it’s commonly known for its ability to generate text, Langchain goes beyond that by introducing agents and tools —two key components that enable more complex, multi-step workflows. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. LangChain provides the smoothest path to high quality agents. Connect language models to apps, automate workflows, and solve complex tasks. The agent returns the observation to the LLM, which can then be used to generate the next action. language_models. . It's designed with simplicity in mind, making it accessible to users without technical expertise, while still offering advanced capabilities for developers. The agent can store, retrieve, and use memories to enhance its interactions with users. chat. Single step: Evaluate any agent step Jun 26, 2025 · Discover how LangChain agents are transforming AI with advanced tools, APIs, and workflows. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. At LangChain, we build tools to help developers build LLM applications, especially those that act as a reasoning engines and interact with external sources of data and computation. Tommie takes Sep 24, 2023 · In the LangChain framework, “Chains” represent predefined sequences of operations aimed at structuring complex processes into a more… create_structured_chat_agent # langchain. We'll start by installing the prerequisite libraries that we'll be using in this example. Besides the actual function that is called, the Tool consists of several components: A Python library for creating hierarchical multi-agent systems using LangGraph. Intended Model Type Whether this agent is intended for Chat Models (takes in messages, outputs message) or LLMs (takes in string, outputs string). How to create tools When constructing an agent, you will need to provide it with a list of Tools that it can use. LangChain has a lot to offer as one of the top frameworks for working with LLMs, supplying LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production. The LangChain community has now implemented some parts of all of those projects in the LangChain framework. Class hierarchy: The agent executes the action (e. al. One of our first applications built was a RetrievalQA system over a Notion database. Mar 1, 2023 · Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). By leveraging agents, you can significantly enhance the capabilities of the OpenAI API and seamlessly integrate external tools. While researching and Dec 26, 2024 · Create a powerful Web-Searching Agent with LangChain for efficient, scalable data retrieval using multiple tools and APIs. Retrieval Augmented Generation (RAG) Part 1: Build an application that uses your own documents to inform its responses. Check out that talk here. Learn how to use LangChain agents and other components to build language applications with chat models, LLMs, tools, and more. js langchain agents Agent Class Agent Abstract Class responsible for calling a language model and deciding an action. Feb 13, 2024 · Plan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. The schemas for the agents themselves are defined in langchain. In this comprehensive guide, we’ll Agents: Build an agent that interacts with external tools. com and LangChain to create an open-source agent in production will serve as a template for future projects. LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. The primary supported way to do this is with LCEL. But more vertical, narrowly scoped, highly controllable agents with custom cognitive architectures. Then, we'll go through the three most effective types of evaluations to run on chat bots: Final response: Evaluate the agent's final response. Instead of relying on predefined scripts, agents analyze user queries and choose agents # Agent is a class that uses an LLM to choose a sequence of actions to take. The role of Agent in LangChain is to help solve feature problems, which include tasks such as numerical operations, web search, and terminal invocation that cannot be handled internally by the language model. Dec 27, 2023 · By harnessing the power of language models through LangChain agents, we can unlock a new era of automation, efficiency, and collaboration. Class hierarchy: This walkthrough showcases using an agent to implement the ReAct logic. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL queries. Trajectory: Evaluate whether the agent took the expected path (e. Nov 22, 2024 · LangChain is a powerful framework designed to build AI-powered applications by connecting language models with various tools, APIs, and data sources. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. BaseLanguageModel, tools: ~typing. ChatPromptTemplate, tools_renderer: ~typing. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. LangChain’s tools and APIs make it easier to set up some impressive uses of natural language processing (NLP) and LLMs (more on that later!). Learn how to build 3 types of planning agents in LangGraph in this post. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. One of its most exciting aspects is the Agents Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. We recommend that you use LangGraph for building agents. In Chains, a sequence of actions is hardcoded. They recognize and prioritize individual tasks, execute LLM invocations and tool interactions, to orchestrate the synthesizing of results. LangChain lets you create copilots that use LLMs to write, act, or wait for approval. Today we are taking a few steps to build towards this vision. Tools are essentially functions that extend the agent’s capabilities by Agents let us do just this. Oct 19, 2024 · At Sequoia’s AI Ascent conference in March, I talked about three limitations for agents: planning, UX, and memory. You can use an agent with a different type of model than it is intended for, but it likely won't produce Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Sequence [~langchain_core. Apr 18, 2023 · Over the past two weeks, there has been a massive increase in using LLMs in an agentic manner. See the full The agent executes the action (e. Sep 9, 2024 · LangChain agents are meta-abstraction combining data loaders, tools, memory, and prompt management. It can recover from errors by running a generated query, catching the traceback and regenerating it Dec 9, 2024 · The schemas for the agents themselves are defined in langchain. Below is a detailed walkthrough of LangChain’s main modules, their roles, and code examples, following the latest How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph Callbacks Callbacks allow you to hook into the various stages of your LLM application's execution. Its architecture allows developers to integrate LLMs with external data, prompt engineering, retrieval-augmented generation (RAG), semantic search, and agent workflows. AgentExecutor [source] # Bases: Chain Agent that is using tools. Create autonomous workflows using memory, tools, and LLM orchestration. The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is Nov 19, 2024 · LangGraph is a multi-agent framework. These are applications that can answer questions about specific source information. In this notebook we'll explore agents and how to use them in LangChain. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. Jun 18, 2024 · Welcome to our latest article on Langchain agents! In this guide, we'll dive into the innovative approach to building agents introduced in Langchain update 0. This will assume knowledge of LLMs and retrieval so if you haven't already explored those sections, it is recommended you do so. LangGraph offers a more flexible and full-featured framework for building agents, including support for tool-calling, persistence of state, and human-in-the-loop workflows. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. base. We finish by listing some roadmap items for the future. Classes The core idea of agents is to use a language model to choose a sequence of actions to take. Apr 4, 2025 · LangChain Agent Framework enables developers to create intelligent systems with language models, tools for external interactions, and more. The main advantages of using SQL Agents are: It can answer questions based on the databases schema as well as on the databases content (like describing a specific table). This notebook goes through how to create your own custom agent. By combining robust building blocks with intelligent orchestrators, LangChain empowers developers to create dynamic, context-aware, and scalable solutions that can transform industries and enhance user experiences. Agent Types This categorizes all the available agents along a few dimensions. Classes Jun 2, 2024 · Conclusion: In this blog, we’ve delved into the LangChain Agent module for developing agent-based applications, exploring various agents and tools while considering conversation history. Aug 5, 2024 · Whether you are developing a conversational agent, an automated research assistant, or a complex data analysis tool, LangChain agents offer a robust solution to enhance your project’s capabilities. The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. load. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. Deploy and scale with LangGraph Platform, with APIs for state management, a visual studio for debugging, and multiple deployment options. A big use case for LangChain is creating agents. In Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. But for certain use cases, how many times we use tools depends on the input. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. Read about all the agent types here. Learn to build AI agents with LangChain and LangGraph. """ # noqa: E501 from __future__ import annotations import json from collections. In this post I will dive more into memory. These applications use a technique known as Retrieval Augmented Generation, or RAG. Feb 27, 2025 · It was create_react_agent, a wrapper for creating a simple tool calling agent. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. Apr 20, 2025 · Frameworks like Agents SDK (and original LangChain, CrewAI, etc) are neither declarative or imperative - they are just abstractions. Different agents have different prompting styles for reasoning, different ways The agent executes the action (e. This walkthrough demonstrates how to use an agent optimized for conversation. This means not only interacting with other LangGraph agents, but all other types of agents as well, regardless of how they are built. It's still not easy to build these agents - but it's entirely possible. Callable [ [~typing. Understand how LangChain agents enhance LLM applications by dynamically integrating external tools, APIs, and real-time data access. That means there are two main considerations when thinking about different multi-agent workflows: What are the multiple independent agents? How are those agents connected? This thinking lends itself incredibly well to a graph representation, such as that provided by langgraph. Jul 4, 2025 · LangChain is a modular framework designed to build applications powered by large language models (LLMs). agent. In these cases, we want to let the model itself decide how many times to use tools and in what order. Today, we are splitting that out of langgraph as part of a 0. Dec 12, 2024 · Build LangChain agents step by step to create AI assistants that automate tasks and integrate advanced tools seamlessly. In chains, a sequence of actions is hardcoded (in code). By integrating tools and crafting intelligent agents, developers can automate complex workflows. The main advantages of using the SQL Agent are: It can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table). We've experimented and pushed the boundary with many different forms of memory, enabling chatbots of all kinds. The goal of tools APIs is to more reliably return valid and useful tool Generative Agents This script implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Park, et. 3 release, and moving it into langgraph-prebuilt. The script below creates two instances of Generative Agents, Tommie and Eve, and runs a simulation of their interaction with their observations. Build controllable agents with LangGraph, our low-level agent orchestration framework. It can recover from errors by running a generated query Feb 24, 2025 · A step-by-step guide on how to build a context-aware agent that fetches real-time data, and deploy it in real-world use cases. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. LangChain’s ecosystem While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. Interested in discussing a Data or AI project? Feel free to reach Chains refer to sequences of calls - whether to an LLM, a tool, or a data preprocessing step. Learn how to build LangChain agents in Python. Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be. They can call external APIs or query databases dynamically, making decisions based on the situation. Customize your agent runtime with LangGraph, explore tools for every task, and debug with LangSmith. prompts. messages import ( AIMessage, BaseMessage Jul 2, 2024 · LangChain agents are responsible for deciding which actions to take and then executing them. Nov 6, 2024 · LangChain is revolutionizing how we build AI applications by providing a powerful framework for creating agents that can think, reason, and take actions. Tool calling agent Tool calling allows a model to detect when one or more tools should be called and respond with the inputs that should be passed to those tools. My Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. serializable import Serializable from langchain_core. BaseTool], prompt: ~langchain_core. , of tool calls) to arrive at the final answer. How to: pass in callbacks at runtime How to: attach callbacks to a module How to: pass callbacks into a module constructor How to: create custom callback handlers How to: use callbacks in Jun 17, 2025 · LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. This includes systems that are commonly referred to as “agents”. Sep 18, 2024 · A key feature of Langchain is its Agents — dynamic tools that enable LLMs to perform tasks autonomously. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. They have an agent abstraction (a Python class) and it contains a bunch of internal logic that runs the agent. create_structured_chat_agent(llm: ~langchain_core. messages import ( AIMessage, BaseMessage, FunctionMessage, HumanMessage, ) Apr 9, 2025 · Learn how to make REST API calls in LangChain agents using custom tools, Python, and best practices for real-world integration. In an API call, you can describe tools and have the model intelligently choose to output a structured object like JSON containing arguments to call these tools. Agents are systems that take a high-level task and use an LLM as a reasoning engine to decide what actions to take and execute those actions. Agents may have access to various tools, and it is crucial to describe these tools so that the agent What is LangChain? LangChain is an open-source framework for developing apps that make use of large language models (LLMs) and similar features, like chatbots. Mar 17, 2025 · In conclusion, LangChain’s tools and agents represent a significant leap forward in the development of AI applications. In this article, we’ll dive into Langchain Agents, their components, and how to use agents # Agent is a class that uses an LLM to choose a sequence of actions to take. Agents select and use Tools and Toolkits for actions. Agent that calls the language model and deciding the action. Oct 15, 2023 · We’re extremely excited about the future of AI agents as they continue to evolve and hope that our partnership with Cal. We'll use the tool calling agent, which is generally the most reliable kind and the recommended one for most use cases. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. structured_chat. In this article, we will explore agents, tools, and the difference between LangChain. And - of course - we've got many types of agents, from the "old" ones that use ReAct style prompting, to newer ones May 29, 2025 · Develop advanced AI agents using LangChain and LangGraph. Jul 1, 2025 · Learn how LangChain agents use reasoning-action loops to tackle complex tasks, integrate tools, and refine outputs in real time. 1. We spend a lot of time thinking about the best infrastructure and developer experience for facilitating this type of communication. This document explains the purpose of the protocol and makes the case for each of the endpoints in the spec. , runs the tool), and receives an observation. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Agent # class langchain. We launched LangGraph This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. Dec 10, 2024 · Agent and multi-agent systems are all about how the different components of the the system communicate with each other. BaseTool]], str] = <function render_text This walkthrough showcases using an agent to implement the ReAct logic. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. Specifically, projects like AutoGPT, BabyAGI, CAMEL, and Generative Agents have popped up. Nov 30, 2023 · LangChain Agents are systems that use an LM to interact with other tools for tasks such as grounded questions-answering or API interaction Jan 3, 2025 · Langchain is an advanced framework that helps developers build sophisticated applications powered by large language models (LLMs). Agents 🤖 Agents are like "tools" for LLMs. Aug 28, 2024 · A comprehensive tutorial on building multi-tool LangChain agents to automate tasks in Python using LLMs and chat models using OpenAI. ptgdw gadmyr qfhyko bzgrba oxcjykeb rtrll ncqs zje ykygc gpoyisk