Langchain action agent. BaseMultiActionAgent # class langchain.

Langchain action agent. It can recover from errors by running a generated query, catching the traceback and regenerating it Dec 9, 2024 · from langchain_core. This agent uses JSON to format its outputs, and is aimed at supporting Chat Models. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. Build copilots that write first drafts for review, act on your behalf, or wait for approval before execution. param Jan 19, 2025 · A deep dive into LangChain's Agent Executor, exploring how to build your custom agent execution loop in LangChain v0. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the Running Agent as an Iterator It can be useful to run the agent as an iterator, to add human-in-the-loop checks as needed. Create an AgentAction. You can use an agent with a different type of model than it is intended for, but it likely won't produce Some language models (like Anthropic's Claude) are particularly good at reasoning/writing XML. note RunnableMultiActionAgent # class langchain. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser Parses tool invocations and final answers in JSON format. AgentActionMessageLog ¶ class langchain_core. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. Why do LLMs need to use Tools? Dec 9, 2024 · langchain. Actions can include: Calling tools (APIs, functions, databases Jan 19, 2025 · A deep dive into LangChain's Agent Executor, exploring how to build your custom agent execution loop in LangChain v0. Using LangChain and LangGraph, you’ll learn how to orchestrate powerful agentic workflows and build dynamic tool-based agents that can search, summarize, and act in complex environments. This is useful when working with ChatModels, and is used to reconstruct conversation history from the agent's perspective. Dec 9, 2024 · langchain_core. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. Tools are essentially functions that extend the agent’s capabilities by Dec 9, 2024 · """Chain that takes in an input and produces an action and action input. Learn how to build 3 types of planning agents in LangGraph in this post. BaseSingleActionAgent [source] # Bases: BaseModel Base Single Action Agent class. log – Additional information to log about the action. agents. BaseMultiActionAgent # class langchain. Callable [ [~typing. Jun 17, 2025 · Build an Agent 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. They enable LLMs to choose actions, call tools, and perform reasoning steps dynamically — like autonomous copilots for your applications. Background This prompt is designed to tag or extract associated thoughts, actions, and observations from ReAct agents, as implemented according to LangChain's guide. ToolAgentAction [source] ¶ Bases: AgentActionMessageLog param log: str [Required] ¶ Additional information to log about the action. 7-mixtral-8x7b-AWQ on my server using vllm the code works almost fine but it shows a strange behavior. May 10, 2023 · TL;DR: We’re introducing a new type of agent executor, which we’re calling “Plan-and-Execute”. Their framework enables you to build layered LLM-powered applications that are context-aware and able to interact dynamically with their environment as agents, leading to simplified code for you and a more dynamic user experience for your customers. Jul 1, 2025 · Learn how LangChain agents use reasoning-action loops to tackle complex tasks, integrate tools, and refine outputs in real time. 🏃 The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. First, it can be used to audit what exactly the LLM predicted to lead to this (tool, tool_input). , runs the tool), and receives an observation. jsAbstract base class for multi-action agents in LangChain. The agent returns the observation to the LLM, which can then be used to generate the next action. agents import AgentExecutor, create_structured_chat_agent from langchain_community. From my experience this will give us more control in reaching a particular tool and create_structured_chat_agent # langchain. May 28, 2025 · Agents are the most powerful abstraction in LangChain. To address these issues and facilitate communication with external applications, we introduce the concept of an Agent as a processor. Classes The agent executes the action (e. We recommend that you use LangGraph for building agents. AgentExecutor [source] # Bases: Chain Agent that is using tools. The core idea of agents is to use a language model to choose a sequence of actions to take. But you can easily control this functionality with handle_parsing_errors! Let's explore how. Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. We will import two last utility functions: a component for formatting intermediate steps (agent action, tool output pairs) to input messages that can be sent to the model, and a component for converting the output message into an agent action/agent finish. Action Planning – It decides whether to answer directly, fetch information from an external tool, or execute a multi-step process. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Raises [ValidationError] [pydantic_core. RunnableMultiActionAgent [source] # Bases: BaseMultiActionAgent Agent powered by Runnables. For a given state of the world it think about what its next immediate action should be, and then does that action. prompts. Expects output to be in one of two formats. Extends the BaseAgent class and provides additional functionality specific to multi-action agents. LangChain provides: A standard interface for agents. chat. BaseTool], prompt: ~langchain_core. I searched the LangChain documentation with the integrated search. It handles direct user requests in a single action. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. AgentActionMessageLog [source] ¶ Bases: AgentAction Representation of an action to be executed by an agent. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Install Packages First, we need to install all the langchain packages. For details, refer to the LangGraph documentation as well as guides for Dec 9, 2024 · langchain. Examples of end-to-end agents. LLMSingleActionAgent ¶ class langchain. BaseSingleActionAgent [source] ¶ Bases: BaseModel Base Single Action Agent class. serializable import Serializable from langchain_core. Create a new model by parsing and validating input data from keyword arguments. The main thing this affects is the prompting strategy used. Apr 3, 2023 · Rather, we consider this the base abstraction for a family of agents that predicts a single action at a time. Tool Execution – The agent interacts with APIs May 1, 2024 · Let’s now create a multi-agent workflow as per the above graph. This log can be used in In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. They can be used for tasks such as grounded question/answering, interacting with APIs, or taking action. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. This idea is largely inspired by BabyAGI and then the "Plan-and-Solve" paper. Custom LLM Agent (with a ChatModel) This notebook goes through how to create your own custom agent based on a chat model. g. Here's an example: task = "Which is bigger? BTS's oldest member's birth month vs. Mar 10, 2024 · 在本文中，我们详细解释了如何使用 LangChain 创建Multi-action Agent。 通过开发自己的代理，您将能够理解复杂的用户查询，将其分解为适当的任务，并有效地处理它们。 """Chain that takes in an input and produces an action and action input. Agent # class langchain. AgentAction [source] ¶ Bases: Serializable Represents a request to execute an action by an agent. Some language models are particularly good at writing JSON. param callback_manager: Optional[BaseCallbackManager] = None ¶ [DEPRECATED] Use callbacks instead. structured_chat. tool_input – The Sep 18, 2024 · This is the most basic type of Langchain Agent, ideal for simple tasks where the agent doesn’t need previous context or planning. A selection of agents to choose from. Agents are used when a single input/output process is not enough, and the task requires reasoning, planning, or interaction with external systems. incumbent Memory in Agent This notebook goes over adding memory to an Agent. param agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, Runnable] [Required] ¶ The agent to run for creating a plan and determining actions to take at each step of the execution loop. ToolAgentAction [source] # Bases: AgentActionMessageLog “Tool agent action. Sequence [~langchain_core. BaseLanguageModel, tools: ~typing. pip install langchain langchain_openai langgraph langchain_core langsmith langchain_experimental We also need to install some packages for storing data (pandas) and creating charts (matplotlib). The action consists of the name of the tool to execute and the input to pass to the tool. In the first call of action, the agent pass educa Observ instead of only educa as action input. BaseTool]], str] = <function render_text Dec 9, 2024 · langchain. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. 1. This log can be used in a few ways. In Chains, a sequence of actions is hardcoded. param log: str [Required] # Additional information to log about the action. tools. You’ll also implement a practical system that processes text from a book, answers queries dynamically and evaluates its This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. The AgentFinish class in the langchain_core. 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. This agent uses a two step process: First, the agent uses an LLM to create a plan to answer Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. ChatPromptTemplate, tools_renderer: ~typing. Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. BaseMultiActionAgent ¶ class langchain. The log is used to pass along extra information about the action. Documentation for LangChain. AgentExecutor [source] ¶ Bases: Chain Agent that is using tools. Create the Agent Putting those pieces together, we can now create the agent. """ message_log: Sequence[BaseMessage] """Similar to log, this can be used to The core idea of agents is to use a language model to choose a sequence of actions to take. Intended Model Type Whether this agent is intended for Chat Models (takes in messages, outputs message) or LLMs (takes in string, outputs string). An LLM agent consists of three parts: PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do LLM: This is the language model that powers the agent stop sequence: Instructs the LLM to stop generating as soon as this string is found OutputParser: This determines Dec 9, 2024 · The schemas for the agents themselves are defined in langchain. This goes over how to use an agent that uses XML when prompting. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. BaseMultiActionAgent [source] ¶ Bases: BaseModel Base Multi Action Agent class. Class hierarchy: [docs] class AgentActionMessageLog(AgentAction): """Representation of an action to be executed by an agent. However, when I run the code, the action tool is applied properly, but does not generate a final answer. output_parsers. The prompt relies on two input variables: {task} and {scratchpad}: task : This is the query that an end user sends to the ReAct agent. Aug 1, 2024 · Hey @krishnazanwar1407! I'm here to help you with any bugs, questions, or contributions you have. The agent executes the action (e. prompts import PromptTemplate template = '''Answer the following questions as best you can. The execution is usually done by a separate agent (equipped with tools). param log: str [Required] ¶ Additional information to log about the action agents # Agent is a class that uses an LLM to choose a sequence of actions to take. Dec 9, 2024 · class langchain. Every agent within a GPTeam simulation has their own unique personality, memories, and directives, leading to interesting emergent behavior as they interact. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them: Memory in LLMChain Custom Agents In order to add a memory to an agent we are going to perform the following steps: We are going to create an LLMChain with memory. Deprecated since version 0. load. In this tutorial, you will learn how to build an autonomous agent powered by large language models (LLMs) by using IBM® Granite™ models and LangChain. 动作代理 Action Agents 📄️ LLMs的MRKL代理 本示例涵盖了如何使用使用ReAct Framework的代理人（基于工具的描述）来决定采取什么行动。 📄️ MRKL聊天模型代理 这个例子介绍了如何使用一个使用ReAct框架 (基于工具描述)来决定采取什么行动的代理。该代理被优化为在聊天模型中使用。如果您想在LLM中使用 ToolAgentAction # class langchain. json. Agents vs Dec 5, 2023 · Master LangChain Agents and React Framework with our ultimate guide! Transform your AI skills, unleash intelligent automation. agent. The schemas for the agents themselves are defined in langchain. Parameters: tool – The name of the tool to execute. param log: str [Required] # Additional information to log about the action Dec 27, 2023 · By harnessing the power of language models through LangChain agents, we can unlock a new era of automation, efficiency, and collaboration. In this case, by default the agent errors. I use a self-host deployment of dolphin-2. While chains in Lang Chain rely on hardcoded sequences of actions, agents use a Custom LLM Agent This notebook goes through how to create your own custom LLM agent. Jan 3, 2025 · An agent in Langchain is a dynamic system that can make decisions based on a given task, interact with external resources (referred to as tools), and perform multiple steps to complete a task. The planning is almost always done by an LLM. Jun 2, 2024 · The core idea behind agents is leveraging a language model to dynamically choose a sequence of actions to take. Classes BaseSingleActionAgent # class langchain. Start learning now! This walkthrough showcases using an agent to implement the ReAct logic. So, are you ready to let your language model take the BaseMultiActionAgent # class langchain. Oct 31, 2023 · Unfortunately, I cannot provide a code. But I can explain in greater detail: My langchain agent is returning its thoughts to the user instead of using them to select the right tool. Abstract class representing an output parser specifically for agent actions and finishes in LangChain. Customize your agent runtime with LangGraph LangGraph provides control for custom agent and multi-agent workflows, seamless human-in-the-loop interactions, and native streaming support for enhanced agent reliability and execution. agents module is used to signal that an agent has reached a stopping condition and is ready to return its final result. We are going to use that LLMChain to create Apr 15, 2025 · 文章浏览阅读682次，点赞25次，收藏5次。前几章中我们已经使用过基础 Agent（如），它能读取用户输入，通过思考（Thought）、调用工具（Action）、获取结果（Observation）不断迭代，直到生成答案（Final Answer）。你可以完全控制 Agent 的提示词，以指导其行为风格、语言偏好、思考策略。AgentType特点与 May 27, 2023 · “Action Agents”: these agents decide an action to take and take that action one step at a time “Plan-and-Execute Agents”: these agents first decide a plan of actions to take, and then execute those actions one at a time. This will result in an AgentAction being returned. I used the GitHub search to find a similar question and 21 hours ago · Learn how to build AI agents using LangChain for retail operations with tools, memory, prompts, and real-world use cases. This is useful when working with ChatModels, and is used to reconstruct conversation history from the agent’s perspective from langchain. Second, it can be used in Sep 10, 2023 · はじめに langchainのAgentは言語モデルに使用する関数（tool)を決定させるためのクラスです。Agentはtoolを決定するだけで実行はしません。タスクを完了するためにはtoolを実行し、その実行結果を言語モデルに渡す必要があり、その処理はAgentではなく Dec 24, 2024 · 文章浏览阅读4k次，点赞18次，收藏28次。在LangChain中，Agent 是一个核心概念，它代表了一种能够利用语言模型（LLM）和其他工具来执行复杂任务的系统。Agent的设计目的是为了处理那些简单的语言模型可能无法直接解决的问题，尤其是当这些任务涉及到多个步骤或者需要外部数据源的情况。Agent 在 Handle parsing errors Occasionally the LLM cannot determine what step to take because its outputs are not correctly formatted to be handled by the output parser. In this simple problem we can demonstrate adding some logic to verify intermediate steps by checking An example repository for getting started with the Agent Inbox and LangGraph - langchain-ai/agent-inbox-langgraph-example Jan 14, 2024 · I tries to make custom LLM with LangChain and want to use a ReAct agent. Dec 9, 2024 · langchain. Base class for single action agents. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI Agent Types This categorizes all the available agents along a few dimensions. pip install pandas matplotlib Import libraries Next, we’ll from langchain_core. base. ToolAgentAction ¶ class langchain. Setup We will be using a wikipedia tool, so need to install that Jun 19, 2025 · AI agents within LangChain take a language model and tie it together with a set of tools to address larger, more complex tasks. agents import Tool, AgentExecutor, BaseMultiActionAgent from langchain import OpenAI, SerpAPIWrapper Apr 18, 2023 · In the traditional LangChain Agent framework (and the AutoGPT framework), the agent thinks one step ahead at a time. AgentExecutor # class langchain. LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. AgentAction [source] # Bases: Serializable Represents a request to execute an action by an agent. 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. Raises ValidationError if the input data cannot be parsed to form a valid model. Further it is returning the action input instead of using it to run my custom function. I'm currently waiting for a human maintainer, but in the meantime, feel free to ask me anything. language_models. BaseSingleActionAgent ¶ class langchain. 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). If the output signals that an action should be taken, should be in the below format. Jan 7, 2024 · LangChain also has another concept called Agent, that we can use to interpret the input from the user and determine which tool or tools to use to complete the task. We’ll explore how agents leverage key components such as memory, planning and action to perform intelligent tasks. We will import two last utility functions: a component for formatting intermediate steps to input messages that can be sent to the model, and an output parser for converting the output message into an agent action/agent finish. Here's a breakdown of how it works Nov 30, 2023 · Agents in LangChain Agents in LangChain are systems that use a language model to interact with other tools. 🤖 What Is an Agent in LangChain? An Agent uses an LLM to decide what action to take based on the input and intermediate results. A SingleActionAgent is used in an our current AgentExecutor. AgentAction # class langchain_core. Plan-and-Execute agents are heavily inspired by BabyAGI and the recent Plan-and-Solve paper. """ from __future__ import annotations import asyncio import builtins import contextlib import json import logging import time from abc import abstractmethod from collections. tool_input – The input to pass in to the Tool. Unlike a static chain of instructions, an agent dynamically decides at each step which action (tool) to take based on the conversation and intermediate results. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the Oct 1, 2024 · Checked other resources I added a very descriptive title to this question. BaseMultiActionAgent [source] # Bases: BaseModel Base Multi Action Agent class. It extends the BaseOutputParser class. In 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. I used the following code to trace the steps the agent take: Aug 28, 2023 · As autonomous agents begin to make their way into production level systems, evaluating an agent’s ability to take a correct action and do… Feb 27, 2025 · I am trying to build a simple react agent using langchain which uses a tool to get the weather condition of a location I tried making my custom react prompt template and used 'create_react_agent' a from langchain import hub from langchain. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. This is similar to AgentAction, but includes a message log consisting of chat messages. 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. ValidationError] if the input data cannot be validated to form a valid model. This is often achieved via tool-calling. LLMSingleActionAgent [source] ¶ Bases: BaseSingleActionAgent Deprecated since version 0. This is to contrast against the previous types of agent we supported, which we’re calling “Action” agents. Instead of relying on predefined scripts, agents analyze user queries and choose Jul 3, 2023 · class langchain. 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. Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. 3. MultiActionAgentOutputParser ¶ Note MultiActionAgentOutputParser implements the standard Runnable Interface. In practice, this means the agent prompts the LLM (the "reasoning engine") to formulate its next action or Jul 22, 2025 · How LangChain Agents Work LangChain Agents operate using a structured workflow that consists of several key components: Input Processing – The agent receives a user query and determines the best way to respond. Classes AI Agents and Applications is your hands-on guide to developing cutting-edge language model solutions for real business needs. In this tutorial we AgentAction # class langchain_core. Feb 16, 2025 · Agents in LangChain are advanced components that enable AI models to decide when and how to use tools dynamically. Agents select and use Tools and Toolkits for actions. AgentAction ¶ class langchain_core. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Feb 13, 2024 · Plan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. create_structured_chat_agent(llm: ~langchain_core. Jun 5, 2023 · On May 16th, we released GPTeam, a completely customizable open-source multi-agent simulation, inspired by Stanford’s ground-breaking “ Generative Agents ” paper from the month prior. messages import ( AIMessage, BaseMessage, FunctionMessage, HumanMessage, ) 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. abc import AsyncIterator, Iterator, Sequence from pathlib import Path from typing import ( Any Dec 9, 2024 · langchain. Jun 23, 2025 · LangChain 创建agent 执行多个action，在现代人工智能的应用场景中，LangChain提供了一种强大的方式来创建代理（agent），使其能够执行多个操作（actions）。本篇博文将深入探讨如何在LangChain中创建这种代理，从背景和核心维度到实战对比和生态扩展，以便不仅帮助你理解这种技术的运作方式，还能掌握 Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. An LLM chat agent consists of three parts: PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do ChatModel: This is the language model that powers the agent stop sequence: Instructs the LLM to stop generating as soon as . To demonstrate the AgentExecutorIterator functionality, we will set up a problem where an Agent must: Retrieve three prime numbers from a Tool Multiply these together. In chains, a sequence of actions is hardcoded (in code). List [~langchain_core. `` ` { Mar 31, 2024 · Should we just remove the validation? @baskaryan @jacoblee93 My logic would be if return_direct=True for that particular tool, we force the multi action agent to return the response directly - if not, then it is able to use multiple tools and continue its though process when tools where return_direct=False. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. """from__future May 2, 2023 · LangChain is a framework for developing applications powered by language models. When the agent reaches a stopping condition, it returns a final return value. Jan 30, 2024 · Description I am trying to use create_react_agent to build the custom agent in this tutorial . zub nhii ifgxt biswg xradix txicd fyd kako zuptj jllms