Langchain agents documentation. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. serializable import Serializable from langchain_core. The agent can store, retrieve, and use memories to enhance its interactions with users. This agent uses a search tool to look up answers to the simpler questions in order to answer the original complex question. This will assume knowledge of LLMs and retrieval so if you haven’t already explored those sections, it is recommended you do so. chat. But for certain use cases, how many times we use tools depends on the input. create_xml_agent(llm: ~langchain_core. Framework to build resilient language agents as graphs. create_csv_agent # langchain_experimental. create_json_chat_agent(llm: ~langchain_core. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. agent_toolkits. csv. It provides essential building blocks like chains, agents, and memory components that enable developers to create sophisticated AI workflows beyond simple prompt-response interactions. LangChain has 208 repositories available. In chains, a sequence of actions is hardcoded (in code). For details, refer to the LangGraph documentation as well as guides for create_json_chat_agent # langchain. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. That's where Agents come in! LangChain comes with a number of built-in agents that are optimized for different use Quick Start 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. Parameters: tool – The name of the tool to execute. This is driven by a LLMChain. tools. messages import ( AIMessage, BaseMessage, FunctionMessage, HumanMessage, ) AgentAction # class langchain_core. Available in both Python- and Javascript-based libraries, LangChain’s tools and APIs simplify the process of building LLM-driven applications like chatbots and AI agents. 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. Parameters: llm (LanguageModelLike) – Language model to use for the agent. 3 days ago · Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows. Discover how each tool fits into the LLM application stack and when to use them. AgentAction [source] # Bases: Serializable Represents a request to execute an action by an agent. The log is used to pass along extra information about the action. Sequence [~langchain_core. agents. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Jul 24, 2025 · Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. agent. BaseTool]], str] = <function render_text_description>, *, stop_sequence An agent that breaks down a complex question into a series of simpler questions. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. 1 billion valuation, helps developers at companies like Klarna and Rippling use off-the-shelf AI models to create new applications. This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. BasePromptTemplate, tools_renderer: ~typing. """ # noqa: E501 from __future__ import annotations import json from typing import Any, List, Literal, Sequence, Union from langchain_core. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. BaseLanguageModel, tools: ~typing. List [~langchain_core. Setup: LangSmith By definition, agents take a self-determined, input Develop, deploy, and scale agents with LangGraph Platform — our purpose-built platform for long-running, stateful workflows. prompts. create_csv_agent(llm: LanguageModelLike, path: str | IOBase | List[str | IOBase], pandas_kwargs: dict | None = None, **kwargs: Any) → AgentExecutor [source] # Create pandas dataframe agent by loading csv to a dataframe. json_chat. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Please scope the permissions of each tools to the minimum required for the application. Intermediate agent actions and tool output messages will be passed in here. Agents Chains are great when we know the specific sequence of tool usage needed for any user input. js to build stateful agents with first-class streaming and human-in-the-loop create_xml_agent # langchain. path (Union[str, IOBase The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. The action consists of the name of the tool to execute and the input to pass to the tool. xml. 1. ChatPromptTemplate, stop_sequence: bool | ~typing. LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. Deprecated since version 0. Jul 9, 2025 · The startup, which sources say is raising at a $1. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. In these cases, we want to let the model itself decide how many times to use tools and in what order. . 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. load. Follow their code on GitHub. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents. BaseTool], prompt: ~langchain_core. LangChain is an open source orchestration framework for application development using large language models (LLMs). It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. Parameters: llm (BaseLanguageModel) – Language model to use for the agent. The schemas for the agents themselves are defined in langchain. Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. LangChain implements a standard interface for large language models and related technologies, such as embedding models and vector stores, and integrates with hundreds of providers. Agent that calls the language model and deciding the action. BaseTool csv_agent # Functionslatest Introduction LangChain is a framework for developing applications powered by large language models (LLMs). Callable [ [~typing. This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly. language_models. 4 days ago · Learn the key differences between LangChain, LangGraph, and LangSmith. Must provide exactly one of ‘toolkit’ or ‘db’. base. Use LangGraph. Tools allow agents to interact with various resources and services like APIs, databases, file systems, etc. tool_input – The Agent # class langchain. Create an AgentAction. If agent_type is “tool-calling” then llm is expected to support tool calling. List [str] = True, tools_renderer: ~typing. yigch demg hdiu qxpguj qgywks ywm isupzcr rraxj krkn svwt