Langchain action agent. Dec 9, 2024 · class langchain. Introduction to agents in LangChain Join my AI Builders community to learn and stay updated on AI: https://whop. AgentActionMessageLog # class langchain_core. 2 the original agent helpers (initialize_agent, AgentExecutor) are deprecated and will only receive critical fixes. In Chains, a sequence of actions is hardcoded. 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). They can be used for tasks such as grounded question/answering, interacting with APIs, or taking action. Agent # class langchain. prompts import PromptTemplate template = '''Answer the following questions as best you can. AgentFinish ¶ class langchain_core. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. """from__future 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. This walkthrough showcases using an agent to implement the ReAct logic. Jul 9, 2025 · LangChain announced that with LangChain 0. This goes over how to use an agent that uses XML when prompting. " The Spectrum of Autonomy The journey from traditional coding to fully autonomous agents represents a fascinating evolution in how we leverage LLMs. BaseSingleActionAgent [source] # Bases: BaseModel Base Single Action Agent class. AgentAction [source] # Bases: Serializable Represents a request to execute an action by an agent. 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 27, 2023 · By harnessing the power of language models through LangChain agents, we can unlock a new era of automation, efficiency, and collaboration. Return type Dict property input_keys: List[str] ¶ Return the input keys. 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 Apr 3, 2023 · Rather, we consider this the base abstraction for a family of agents that predicts a single action at a time. LangChain recommends moving to LangGraph’s node‑based approach for better control flow, built‑in persistence, and the ability to use multi‑actor workflows. Classes 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… Sep 9, 2024 · Agents: A higher order abstraction that uses an LLMs reasoning capabilities for structuring a complex query into several distinct tasks. 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. param log: str [Required] # Additional information to log about the action. ChatPromptTemplate, tools_renderer: ~typing. Oct 31, 2023 · Unfortunately, I cannot provide a code. Jul 24, 2024 · 文章浏览阅读1. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. 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 import Tool, AgentExecutor, BaseMultiActionAgent from langchain import OpenAI, SerpAPIWrapper BaseMultiActionAgent # class langchain. The agent returns the observation to the LLM, which can then be used to generate the next action. Base class for single action agents. Apr 18, 2023 · In the traditional LangChain Agent framework (and the AutoGPT framework), the agent thinks one step ahead at a time. tool_input – The Dec 9, 2024 · langchain_core. This is useful when working with ChatModels, and is used to reconstruct conversation history from the agent's perspective. chat. This log can be used Custom LLM Agent This notebook goes through how to create your own custom LLM agent. AgentActionMessageLog [source] ¶ Bases: AgentAction Representation of an action to be executed by an agent. g. Dec 9, 2024 · tool_run_logging_kwargs() → Dict ¶ Return logging kwargs for tool run. AgentFinish [source] ¶ Bases: Serializable Final return value of an ActionAgent. To address these issues and facilitate communication with external applications, we introduce the concept of an Agent as a processor. agent. LangChain provides: A standard interface for agents. param log: str [Required] ¶ Additional information to log about the return value. Raises [ValidationError] [pydantic_core. The log is used to pass along extra information about the action. This is useful when working with ChatModels, and is used to reconstruct conversation history from the agent’s perspective. The AgentFinish class in the langchain_core. But you can easily control this functionality with handle_parsing_errors! Let's explore how. Dec 24, 2024 · 文章浏览阅读3. 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 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. json. This is similar to AgentAction, but includes a message log consisting of chat messages. BaseMultiActionAgent ¶ class langchain. In Jun 2, 2024 · The core idea behind agents is leveraging a language model to dynamically choose a sequence of actions to take. """ from __future__ import annotations import asyncio import builtins import contextlib import json import logging import time from abc import abstractmethod from collections. Raises ValidationError if the input data cannot be parsed to form a valid model. This idea is largely inspired by BabyAGI and then the "Plan-and-Solve" paper. Expects output to be in one of two formats. Documentation for LangChain. Class hierarchy: [docs] class AgentActionMessageLog(AgentAction): """Representation of an action to be executed by an agent. This is used to pass This walkthrough demonstrates how to use an agent optimized for conversation. language_models. LLMSingleActionAgent ¶ class langchain. LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. Understand how LangChain agents enhance LLM applications by dynamically integrating external tools, APIs, and real-time data access. Classes Dec 9, 2024 · langchain. The action consists of the name of the tool to execute and the input to pass to the tool. The execution is usually done by a separate agent (equipped with tools). ValidationError] if the input data cannot be validated to form a valid model. This log can be used in AgentAction # class langchain_core. This is useful when working with ChatModels, and is used to reconstruct conversation history from the agent’s perspective Agent # class langchain. 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. 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. Dec 9, 2024 · """Chain that takes in an input and produces an action and action input. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. May 2, 2023 · LangChain is a framework for developing applications powered by language models. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI 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. . Extends the BaseSingleActionAgent class and provides methods for planning agent actions based on LLMChain outputs. BaseTool], prompt: ~langchain_core. com/ai-buildermore BaseSingleActionAgent # class langchain. 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. This is used for agents that can return multiple actions. BaseLanguageModel, tools: ~typing. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser Parses tool invocations and final answers in JSON format. BaseMultiActionAgent [source] ¶ Bases: BaseModel Base Multi Action Agent class. 1. 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. 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. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. The planning is almost always done by an LLM. ToolAgentAction # class langchain. Create an AgentAction MultiActionAgentOutputParser # class langchain. While chains in Lang Chain rely on hardcoded sequences of actions, agents use a Dec 9, 2024 · langchain. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Dec 3, 2023 · LangChain系列文章 LangChain 实现给动物取名字， LangChain 2模块化prompt template并用streamlit生成网站 实现给动物取名字 LangChain 3使用Agent访问Wikipedia和llm-math计算狗的平均年龄 LangChain 4用向量数据库Faiss存储，读取YouTube的视频文本搜索Indexes for information retrieve LangChain 5易速鲜花内部问答系统 LangChain 6根据图片 Class representing a single action agent using a LLMChain in LangChain. , runs the tool), and receives an observation. AgentExecutor [source] # Bases: Chain Agent that is using tools. Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. In the first call of action, the agent pass educa Observ instead of only educa as action input. 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. """ message_log: Sequence[BaseMessage] """Similar to log, this can be used to Jan 6, 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. Learn how to build 3 types of planning agents in LangGraph in this post. Mar 28, 2024 · While you may have come across information about Agents in the LangChain documentation, understanding the intricate workings of these agents might still be unclear. 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. 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 Dec 5, 2023 · Master LangChain Agents and React Framework with our ultimate guide! Transform your AI skills, unleash intelligent automation. prompts. Start learning now! Deprecated since version 0. In chains, a sequence of actions is hardcoded (in code). ToolAgentAction [source] # Bases: AgentActionMessageLog Create an AgentAction. 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 Aug 1, 2024 · Hey @krishnazanwar1407! I'm here to help you with any bugs, questions, or contributions you have. Setup We will be using a wikipedia tool, so need to install that 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. Create autonomous workflows using memory, tools, and LLM orchestration. base. tools. A big use case for LangChain is creating agents. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. BaseMultiActionAgent # class langchain. So, are you ready to let your language model take the Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. 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. RunnableMultiActionAgent [source] # Bases: BaseMultiActionAgent Agent powered by Runnables. One of its most exciting aspects is the Agents At the heart of LangChain's innovation is its concept of agents, succinctly defined by LangChain's founder Harrison Chase as "a system that uses an LLM to decide the control flow of an application. Why do LLMs need to use Tools? Jan 30, 2024 · Description I am trying to use create_react_agent to build the custom agent in this tutorial . BaseSingleActionAgent [source] ¶ Bases: BaseModel Base Single Action Agent class. I'm currently waiting for a human maintainer, but in the meantime, feel free to ask me anything. Build copilots that write first drafts for review, act on your behalf, or wait for approval before execution. Nov 30, 2023 · Agents in LangChain Agents in LangChain are systems that use a language model to interact with other tools. 1w次，点赞47次，收藏60次。langchain 中提供了内置工具的，但是基本不能用，除了一个计算器和一个执行 python 代码的，其他的都要 apiTool 模块相当于是使用外部工具，或者自定义工具。_langchain agent tool The agent executes the action (e. Parameters: tool – The name of the tool to execute. 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 May 10, 2023 · TL;DR: We’re introducing a new type of agent executor, which we’re calling “Plan-and-Execute”. agents import AgentExecutor, create_structured_chat_agent from langchain_community. AgentAction [source] ¶ Bases: Serializable Represents a request to execute an action by an agent. RunnableMultiActionAgent # class langchain. Tools are essentially functions that extend the agent’s capabilities by create_structured_chat_agent # langchain. `` ` { from langchain_core. BaseSingleActionAgent ¶ class langchain. Extends the BaseAgent class and provides additional functionality specific to multi-action agents. 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. Agents vs Dec 9, 2024 · langchain_core. Every agent within a GPTeam simulation has their own unique personality, memories, and directives, leading to interesting emergent behavior as they interact. Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. BaseTool]], str] = <function render_text By themselves, language models can't take actions - they just output text. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. Create an AgentAction. The schemas for the agents themselves are defined in langchain. tool_input – The input to pass in to the Tool. Callable [ [~typing. It can recover from errors by running a generated query, catching the traceback and regenerating it 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. From my experience this will give us more control in reaching a particular tool and from langchain_core. output_parsers. serializable import Serializable from langchain_core. Dec 9, 2024 · langchain_core. There’s no long technical deep dives or academic jargon—the book’s pragmatic approach gives you just enough theory to start building working apps. 8k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 Abstract base class for single action agents in LangChain. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. This is to contrast against the previous types of agent we supported, which we’re calling “Action” agents. 代理（ Agent ）是围绕模型的封装，它接收用户输入并返回与 “动作 action ”相对应的响应，以及相应的 “动作输入 action input ”。 Apr 27, 2023 · The code below shows a complete working example of a LangChain Agent answering an extremely ambiguous and complex question: Who is regarded as the father of the iPhone and what is the square root of his year of birth? The agent has a few actions at its disposal: LLM Math, SerpApi, below is a screenshot of the SerpApi website. Agents select and use Tools and Toolkits for actions. SerpApi makes data extraction from search engine results actionable Dec 9, 2024 · class langchain. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Dec 9, 2024 · langchain. This is driven by a LLMChain. In this case, by default the agent errors. Returns List of input keys. A selection of agents to choose from. Deprecated since version 0. jsAbstract base class for multi-action agents in LangChain. Override init to support instantiation by position for backward compat. In this comprehensive guide, we’ll Learn to build AI agents with LangChain and LangGraph. Some language models (like Anthropic's Claude) are particularly good at reasoning/writing XML. 7-mixtral-8x7b-AWQ on my server using vllm the code works almost fine but it shows a strange behavior. Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. create_structured_chat_agent(llm: ~langchain_core. Jun 17, 2025 · In this tutorial we will build an agent that can interact with a search engine. property return_values: List[str] ¶ Return values of the agent. AgentExecutor # class langchain. LLMSingleActionAgent [source] ¶ Bases: BaseSingleActionAgent Deprecated since version 0. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. Extends the BaseAgent class and provides additional functionality specific to single action agents. BaseSingleActionAgent # class langchain. Here's a breakdown of how it works 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. AI Agents and Applications teaches you to use the user-friendly LangChain library to create your own research assistants, information enterprise chatbots, and more. structured_chat. Classes The core idea of agents is to use a language model to choose a sequence of actions to take. AgentActionMessageLog ¶ class langchain_core. When the agent reaches a stopping condition, it returns a final return value. load. This post aims to delve Jul 1, 2025 · Learn how LangChain agents use reasoning-action loops to tackle complex tasks, integrate tools, and refine outputs in real time. Examples of end-to-end agents. 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。 Feb 13, 2024 · Plan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. Agents return an AgentFinish when they have reached a stopping condition. LangChain can parse LLM output to identify tasks, and then query an LLM repetitively until all tasks are completed, thereby synthesizing intermediate results into a final answer. abc import AsyncIterator, Iterator, Sequence from pathlib import Path from typing import ( Any Dec 9, 2024 · The schemas for the agents themselves are defined in langchain. I used the following code to trace the steps the agent take: from langchain import hub from langchain. 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 Abstract class representing an output parser specifically for agent actions and finishes in LangChain. Further it is returning the action input instead of using it to run my custom function. messages import ( AIMessage, BaseMessage, FunctionMessage, HumanMessage, ) from langchain. If the output signals that an action should be taken, should be in the below format. A SingleActionAgent is used in an our current AgentExecutor. Create the Agent Putting those pieces together, we can now create the agent. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Sequence [~langchain_core. 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. agents module is used to signal that an agent has reached a stopping condition and is ready to return its final result. It extends the BaseOutputParser class. 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. The agent executes the action (e. AgentAction # class langchain_core. For a given state of the world it think about what its next immediate action should be, and then does that action. I use a self-host deployment of dolphin-2. agents. For details, refer to the LangGraph documentation as well as guides for Learn how to build LangChain agents in Python. Plan-and-Execute agents are heavily inspired by BabyAGI and the recent Plan-and-Solve paper. There are several key components here: Schema LangChain has several abstractions to make working with agents easy The core idea of agents is to use a language model to choose a sequence of actions to take. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Create a new model by parsing and validating input data from keyword arguments. MultiActionAgentOutputParser [source] ¶ Bases: BaseOutputParser [Union [List [AgentAction], AgentFinish]] Base class for parsing agent output into agent actions/finish. We recommend that you use LangGraph for building agents. 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. Agent that calls the language model and deciding the action. List [~langchain_core. 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. BaseMultiActionAgent [source] # Bases: BaseModel Base Multi Action Agent class. This will result in an AgentAction being returned. log – Additional information to log about the action. AgentActionMessageLog [source] # Bases: AgentAction Representation of an action to be executed by an agent. Class hierarchy: Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. AgentAction ¶ class langchain_core. MultiActionAgentOutputParser [source] # Bases: BaseOutputParser[Union[list[AgentAction], AgentFinish]] Base class for parsing agent output into agent actions/finish. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. Classes """Chain that takes in an input and produces an action and action input. qtvil lpm unpjvan ghm gdieffrk wxgzgzg bsx gjreb fwjjn bsr