Langchain agents documentation template. The agent returns the exchange.
- Langchain agents documentation template. Examples Summarization Use case Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc. With templates, you clone the repo - you then have access to all the code, so you can change prompts, chaining logic, and do anything else you want! Default is render_text_description. You can access that version of the documentation in the v0. g. Sequence [~langchain_core. . structured_chat. 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: await callbacks langgraph langgraph is an extension of langchain aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Use to create flexible templated prompts for chat models. We recommend that you use LangGraph for building agents. Each approach has distinct strengths Introduction LangChain is a framework for developing applications powered by large language models (LLMs). These are applications that can answer questions about specific source information. The difference between the two is that the tools API allows the model to request that multiple functions be invoked at once, which can reduce response times in some architectures. Those sample documents are based on the conceptual guides for Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. May 31, 2025 · Learn to build custom LangChain agents for specific domains. The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly. ChatPromptTemplate, tools_renderer: ~typing. AgentScratchPadChatPromptTemplate # class langchain. I implement and compare three main architectures: Plan and Execute, Multi-Agent Supervisor Multi-Agent Collaborative. ChatPromptTemplate[source] # Bases: BaseChatPromptTemplate Prompt template for chat models. PromptTemplate [source] # Bases: StringPromptTemplate Prompt template for a language model. This tutorial demonstrates text summarization using built-in chains and LangGraph. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. ) and you want to summarize the content. OpenAI API has deprecated functions in favor of tools. The agent can store, retrieve, and use memories to enhance its interactions with users. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. js template - template LangChain. Productionization: Use LangSmith to inspect, monitor Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. If you're looking to get started with chat models, vector stores, or other LangChain components from a specific provider, check out our supported integrations. This is a multi-part tutorial: Part 1 (this guide) introduces RAG Mar 31, 2024 · The basic architecture is to setup a document agent of each of the documents, with each document agent being able to perform question answering and summarisation within its own document. They are all in a standard format which make it easy to deploy them with LangServe. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. One common use-case is extracting data from text to insert into a database or use with some other downstream system. 1. Introduction LangChain is a framework for developing applications powered by large language models (LLMs). This state management can take several forms, including: Simply stuffing previous messages into a chat model prompt. Chat models and prompts: Build a simple LLM application with prompt templates and chat models. You can peruse LangSmith how-to guides here, but we'll highlight a few sections that are particularly relevant to LangChain below: Evaluation Hypothetical Document Embeddings: A retrieval technique that generates a hypothetical document for a given query, and then uses the embedding of that document to do semantic search. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. 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. js to build stateful agents with first-class streaming and human-in-the-loop langchain: 0. Prompt Templates Prompt templates help to translate user input and parameters into instructions for a language model. Ollama allows you to run open-source large language models, such as Llama 2, locally. In Here's an example: . Must provide exactly one of ‘toolkit’ or ‘db’. How-To Guides We Develop, deploy, and scale agents with LangGraph Platform — our purpose-built platform for long-running, stateful workflows. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. Reference: API reference documentation for all Agent classes. In this walkthrough we'll go over how to perform document summarization using LLMs. language_models. , runs the tool), and receives an observation. Quickstart In this quickstart we'll show you how to: Get setup with LangChain, LangSmith and LangServe Use the most basic and common components of LangChain: prompt templates, models, and output parsers Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining Build a simple application with LangChain Trace your application with Dec 9, 2024 · from langchain_core. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. We've worked with some of our partners to create a set of easy-to-use templates to help developers get to production more quickly. This template uses a csv agent with tools (Python REPL) and memory (vectorstore) for interaction (question-answering) with text data. 2 docs. AgentScratchPadChatPromptTemplate [source] # Bases: ChatPromptTemplate Chat prompt template for the Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. Paper. Custom LLM Agent This notebook goes through how to create your own custom LLM agent. If an empty list is provided (default), a list of sample documents from src/sample_docs. 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. The core logic, defined in src/react_agent/graph. code-block:: python from langchain_core. Overview A central question for building a summarizer is how to pass Build a Retrieval Augmented Generation (RAG) App: Part 1 One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. LangChain provides the smoothest path to high quality agents. When the agent One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. This guide covers a few strategies for getting structured outputs from a model. In this comprehensive guide, we’ll Jun 16, 2025 · Prompt templating is essential for guiding language models to produce precise, context-aware outputs, with LangChain offering dynamic and reusable templates for scalability and efficiency. Build a simple LLM application with chat models and prompt templates In this quickstart we’ll show you how to build a simple LLM application with LangChain. Use LangGraph. 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. Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in. agents. , a tool to run). Prompt Templates output It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build. The agent returns the exchange rate between two currencies on a specified date. LLMs are a great tool for this given their proficiency in understanding and synthesizing text. Agent that calls the language model and deciding the action. LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows. AgentExecutor [source] # Bases: Chain Agent that is using tools. This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. js to build stateful agents with first-class streaming and human-in-the-loop In this quickstart we'll show you how to build a simple LLM application with LangChain. Examples ChatPromptTemplate # class langchain_core. For details, refer to the LangGraph documentation as well as guides for AgentExecutor # class langchain. json is indexed instead. Build controllable agents with LangGraph, our low-level agent orchestration framework. The retrieval chat bot manages a chat history and This tutorial previously used the RunnableWithMessageHistory abstraction. BaseTool]], str] = <function render It is often useful to have a model return output that matches a specific schema. LangChain + Next. Prompts A prompt for a language model is a set of instructions or input provided by a user to guide the model's response, helping it understand the context and generate relevant and coherent language-based output, such as answering questions, completing sentences, or engaging in a conversation. This guide will cover how to bind tools to an LLM, then invoke the LLM to generate these arguments. For working with more advanced agents, we'd recommend checking out LangGraph Agents or the migration guide create_structured_chat_agent # langchain. For detailed documentation of all SQLDatabaseToolkit features and configurations head to the API reference. agent. While GPT can generate and understand natural language, LangChain enables it to: Interact with external APIs and databases Maintain memory across conversations Chain multiple calls together for multi-step reasoning Integrate with tools and agents for dynamic workflows SQLDatabase Toolkit This will help you get started with the SQL Database toolkit. 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 The core idea of agents is to use a language model to choose a sequence of actions to take. Step-by-step guide with code examples, tools, and deployment strategies for AI automation. Productionization How to add memory to chatbots A key feature of chatbots is their ability to use the content of previous conversational turns as context. 27 # Main entrypoint into package. Below we assemble a minimal SQL agent. abc. It showcases how to use and combine LangChain modules for several use cases. Starter template and example use-cases for LangChain projects in Next. 3. py, demonstrates a flexible ReAct agent that iteratively One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. Returns: A Runnable sequence representing an agent. These templates serve as a set of reference architectures for a wide variety of popular LLM use cases. Deploy and scale with LangGraph Platform, with APIs for state management, a visual studio for debugging, and multiple deployment options. Prompt Templates output a LangChain implements standard interfaces for defining tools, passing them to LLMs, and representing tool calls. This application will translate text from English into another language. Next, we will use the high level constructor for this type of agent. Using LangGraph's pre-built ReAct agent constructor, we can do this in one line. js application Social media agent - agent for sourcing, curating, and scheduling social media posts with human-in-the-loop (TypeScript) Agent Protocol - Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production Jun 6, 2025 · What is LangChain? LangChain is a framework designed to help developers build applications powered by language models. It can recover from errors by running a generated query, catching the traceback and regenerating it LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. Familiarize yourself with LangChain's open-source components by building simple applications. It accepts a set of parameters from the user that can be used to generate a prompt for a language model. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. ChatPromptTemplate [source] # Bases: BaseChatPromptTemplate Prompt template for chat models. This template creates an agent that uses OpenAI function calling to communicate its decisions on what actions to take. 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. Callable [ [list [~langchain_core. More complex modifications Apr 24, 2024 · This section will cover building with the legacy LangChain AgentExecutor. This project explores multiple multi-agent architectures using Langchain (LangGraph), focusing on agent collaboration to solve complex problems. BaseTool], prompt: ~langchain_core. It contains example graphs exported from src/retrieval_agent/graph. 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. note Deprecated since version 0. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. Agents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. Still, this is a great way to get started with LangChain - a lot of This is a starter project to help you get started with developing a RAG research agent using LangGraph in LangGraph Studio. Hit the ground running using third-party integrations and Templates. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. A big use case for LangChain is creating agents. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. The template can be formatted using either f-strings (default), jinja2, or mustache syntax This notebook goes through how to create your own custom agent. A common application is to enable agents to answer questions using data in a relational database, potentially in an May 31, 2025 · Learn to build custom LangChain agents for specific domains. LangSmith documentation is hosted on a separate site. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the development of advanced language model-based applications. Parameters: llm (BaseLanguageModel) – Language model to use for the agent. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. This is a relatively simple LLM application - it’s just a single LLM call plus some prompting. 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 Agents use language models to choose a sequence of actions to take. Oct 31, 2023 · LangChain Templates offers a collection of easily deployable reference architectures that anyone can use. chat. prompt. In this article we will learn more about complete LangChain ecosystem. BaseLanguageModel, tools: ~collections. prompts import PromptTemplate template = '''Answer the following questions as best you can. If agent_type is “tool-calling” then llm is expected to support tool calling. Deprecated since version 0. 20 hours ago · Learn how to build AI agents using LangChain for retail operations with tools, memory, prompts, and real-world use cases. js starter app. Oct 31, 2023 · LangChain Templates are the easiest and fastest way to build a production-ready LLM application. It provides a set of tools and components that enable seamless integration of large language models (LLMs) with other data sources, systems and services. Main Libraries in the LangChain Ecosystem This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. Dec 9, 2024 · langchain. 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. js, including chat, agents, and retrieval. What Is This Template? Agents use language models to choose a sequence of actions to take. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. The agent executes the action (e. Prompt Templates take as input an object, where each key represents a variable in the prompt template to fill in. The agent returns the observation to the LLM, which can then be used to generate the next action. tools. Jun 17, 2025 · In this tutorial we will build an agent that can interact with a search engine. Using agents This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. with_structured_output() method Sep 19, 2024 · We chose templates because this makes it easy to modify the inner functionality of the agents. 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 okay to finish. Besides the actual function that is called, the Tool consists of several components:. Tools are essentially functions that extend the agent’s capabilities by In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. py that implement a retrieval-based question answering system. It's recommended to use the tools agent for OpenAI models. The agent returns the exchange Welcome to the LangChain Template repository! This template is designed to help developers quickly get started with the LangChain framework, providing a modular and scalable foundation for building powerful language model-driven applications. Agent ¶ class langchain. For details, refer to the LangGraph documentation as well as guides for 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. These highlight how to integrate various types of tools, how to work with different types of agents, and how to customize agents. Agent [source] ¶ Bases: BaseSingleActionAgent Deprecated since version 0. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. Build a simple LLM application with chat models and prompt templates In this quickstart we'll show you how to build a simple LLM application with LangChain. We will equip it with a set of tools using LangChain's SQLDatabaseToolkit. For the external knowledge source, we will use the same LLM Powered Autonomous Agents blog post by Lilian Weng from the RAG tutorial. Agents select and use Tools and Toolkits for actions. Here are the steps: Define and configure a model Define and use a tool (Optional) Store chat history (Optional) Customize the prompt template (Optional How to create tools When constructing an agent, you will need to provide it with a list of Tools that it can use. base. Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. js + Next. These applications use a technique known as Retrieval Augmented Generation, or RAG. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents. prompts. It takes as input all the same input variables as the prompt passed in does. 3 days ago · This page shows you how to develop an agent by using the framework-specific LangChain template (the LangchainAgent class in the Vertex AI SDK for Python). Jun 17, 2025 · In this tutorial we will build an agent that can interact with a search engine. Default is TEMPLATE_TOOL_RESPONSE. This template scaffolds a LangChain. Agent # class langchain. Finally, we will walk through how to construct a conversational retrieval agent from components. 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). The . js application Social media agent - agent for sourcing, curating, and scheduling social media posts with human-in-the-loop (TypeScript) Agent Protocol - Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production Agents, in which we give an LLM discretion over whether and how to execute a retrieval step (or multiple steps). schema. Rewrite-Retrieve-Read: A retrieval technique that rewrites a given query before passing it to a search engine. Still, this is a great way to get started with LangChain - a lot of features can be built with just some prompting and an LLM call! This notebook walks through a few ways to customize conversational memory. Specifically: Simple chat Returning structured output from an LLM call Answering complex, multi-step questions with agents Retrieval augmented generation (RAG) with a chain and a vector store Retrieval augmented generation (RAG) with an agent and a vector This walkthrough showcases using an agent to implement the ReAct logic. 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. Quickstart This quick start provides a basic overview of how to work with prompts. PromptTemplate # class langchain_core. LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. This is a starter project to help you get started with developing a retrieval agent using LangGraph in LangGraph Studio. This is driven by a LLMChain. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks and components. A prompt template consists of a string template. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. Tools within the SQLDatabaseToolkit are designed to interact with a SQL database. template_tool_response (str) – Template prompt that uses the tool response (observation) to make the LLM generate the next action to take. They can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table). A basic agent works in the following manner: Given a prompt an agent uses an LLM to request an action to take (e. ChatPromptTemplate # class langchain_core. create_structured_chat_agent(llm: ~langchain_core. nja jqc gede uuipm rcq kib gls psw biidfl dzvche