Langchain agents documentation example. In this comprehensive guide, we’ll .
- Langchain agents documentation example. Deprecated since version 0. load_tools # langchain_community. Many popular Ollama models are chat completion models. Contribute to langchain-ai/langgraph development by creating an account on GitHub. May 2, 2023 · LangChain is a framework for developing applications powered by language models. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. Read about all the agent types here. Jul 9, 2025 · The startup, which sources say is raising at a $1. Classes Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. 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. This guide will cover how to bind tools to an LLM, then invoke the LLM to generate these arguments. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. This is a multi-part tutorial: Part 1 (this guide) introduces RAG Introduction LangChain is a framework for developing applications powered by large language models (LLMs). It's designed to be simple yet informative, guiding you through the essentials of integrating custom tools with Langchain. 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 Custom agent This notebook goes through how to create your own custom agent. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Oct 13, 2023 · With easy-to-follow instructions and lucid examples, I’ll guide you through the intricate world of LangChain, unlocking its immense potential. Finally, it creates a LangChain Document for each page of the PDF with the page's content and some metadata about where in the document the text came from. This guide will help you migrate your existing v0. Build a multi-agent system You can use handoffs in any agents built with LangGraph. We'll use the tool calling agent, which is generally the most reliable kind and the recommended one for most use cases. For working with more advanced agents, we'd recommend checking out LangGraph Agents or the migration guide Agents You can pass a Runnable into an agent. It provides essential building blocks like chains, agents, and memory components that enable developers to create sophisticated AI workflows beyond simple prompt-response interactions. It seamlessly integrates with LangChain, and you can use it to inspect and debug individual steps of your chains as you build. 0 chains LangChain has evolved since its initial release, and many of the original "Chain" classes have been deprecated in favor of the more flexible and powerful frameworks of LCEL and LangGraph. AgentExecutor [source] # Bases: Chain Agent that is using tools. with_structured_output() method 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. Use LangGraph. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. The agent executes the action (e. LangChain is an open source orchestration framework for application development using large language models (LLMs). It is often useful to have a model return output that matches a specific schema. agents. output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Stay ahead with this up-to-the-minute resource and start your LLM development journey now. Tools are essentially functions that extend the agent’s capabilities by Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. A basic agent works in the following manner: Given a prompt an agent uses an LLM to request an action to take (e. 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. It can often be useful to have an agent return something with more structure. Whether you’re an indie developer experimenting with AI apps or a company needing offline capabilities, this setup is highly customizable and production-ready with the right tooling. This should be pretty tightly coupled to the instructions in the prompt. 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. Sep 12, 2024 · 5 Real Word Examples - How Do Custom LangChain Agents Work? LangChain Agents, with their dynamic and adaptive capabilities, have opened up a new frontier in the development of LLM and AI-powered applications. Tools can be passed to chat models that support tool calling allowing the model to request the execution of a specific function with specific inputs. 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! LangChain 🔌 MCP. This tutorial previously used the RunnableWithMessageHistory abstraction. It can be used for chatbots, text summarisation, data generation, code understanding, question answering, evaluation, and more. LangChain allows AI developers to develop applications based on the combined Large Language Models (such as GPT-4) with external sources of Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. 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. 0 chains to the new abstractions. End-to-end Example: Question Answering over Notion Database 💬 Chatbots Documentation End-to-end Example: Chat-LangChain 🤖 Agents Documentation End-to-end Example: GPT+WolframAlpha Getting Started # Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application. You can order the results by a relevant column to return the most Parameters: llm (BaseLanguageModel) – LLM to use as the agent. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. load_tools(tool_names: List[str], llm: BaseLanguageModel | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, allow_dangerous_tools: bool = False, **kwargs: Any) → List[BaseTool] [source] # Load tools based on their name. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. 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 the first things to do when building an agent is to decide what tools it should have access to. tools (Sequence[BaseTool]) – Tools this agent has access to. 4 days ago · Learn the key differences between LangChain, LangGraph, and LangSmith. This application will translate text from English into another language. 17 ¶ langchain. Jan 31, 2025 · Learn how to build a Retrieval-Augmented Generation (RAG) application using LangChain with step-by-step instructions and example code create_csv_agent # langchain_experimental. LangSmith documentation is hosted on a separate site. agents ¶ Agent is a class that uses an LLM to choose a sequence of actions to take. 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. By understanding the core architecture — LLMs, tools, chains, memory, and the agent loop — developers can create sophisticated agents tailored to specific use cases. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. 0 in January 2024, is your key to creating your first agent with Python. 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. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. This state management can take several forms, including: Simply stuffing previous messages into a chat model prompt. LangChain has 208 repositories available. You can access that version of the documentation in the v0. Next, we will use the high level constructor for this type of agent. 2 docs. But for certain use cases, how many times we use tools depends on the input. This is generally the most reliable way to create agents. tool_names: contains all tool names. Besides the actual function that is called, the Tool consists of several components: This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. prompt (BasePromptTemplate) – The prompt to use. Additional examples such as streaming, async invocation and function calling can be found in the LangChain documentation. Parameters: llm (LanguageModelLike) – Language model to use for the agent. # pip install wikipedia from langchain. Prompt Templates output Introduction LangChain is a framework for developing applications powered by large language models (LLMs). The core idea of agents is to use a language model to choose a sequence of actions to take. We recommend using the prebuilt agent or ToolNode, as they natively support handoffs tools returning Command. Intermediate agent actions and tool output messages will be passed in here. How to migrate from v0. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. You are currently on a page documenting the use of Ollama models as text completion models. It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. 3 days ago · Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows. 1. param log: str [Required] # Additional information to log about the action. Using agents This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. Class hierarchy: Apr 25, 2023 · Below is an example in which the agent first looks up the date of Barack Obama’s birth with Wikipedia and then calculates his age in 2022 with a calculator. LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows. 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). Return type: In this quickstart we'll show you how to build a simple LLM application with LangChain. 1 billion valuation, helps developers at companies like Klarna and Rippling use off-the-shelf AI models to create new applications. For working with more advanced agents, we’d recommend checking out LangGraph. For details, refer to the LangGraph documentation as well as guides for AgentExecutor # class langchain. load_tools. 27 # Main entrypoint into package. The agent returns the observation to the LLM, which can then be used to generate the next action. Discover how each tool fits into the LLM application stack and when to use them. base. Question answering with RAG LangChain and LangGraph SQL agents example. See Prompt section below for more. This repository contains sample code to demonstrate how to create a ReAct agent using Langchain. Contribute to langchain-ai/langchain-mcp-adapters development by creating an account on GitHub. . Getting Started Apr 2, 2025 · You can replace the endpoint to your custom model deployed on the serving endpoint. Instead of relying on predefined scripts, agents analyze user queries and choose LangChain implements standard interfaces for defining tools, passing them to LLMs, and representing tool calls. \nYou have access to the following tools which help you learn more about the JSON Jun 4, 2025 · Using a Langchain agent with a local LLM offers a compelling way to build autonomous, private, and cost-effective AI workflows. path (Union[str, IOBase It then extracts text data using the pypdf package. In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. For details, refer to the LangGraph documentation as well as guides for Nov 15, 2023 · A Complete LangChain tutorial to understand how to create LLM applications and RAG workflows using the LangChain framework. Productionization In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. create_json_agent(llm: BaseLanguageModel, toolkit: JsonToolkit, callback_manager: BaseCallbackManager | None = None, prefix: str = 'You are an agent designed to interact with JSON. Prompt Templates take as input a dictionary, where each key represents a variable in the prompt template to fill in. An AgentExecutor with the specified agent_type agent and access to a PythonAstREPLTool with the loaded DataFrame (s) and any user-provided extra_tools. Here’s an example: Dec 9, 2024 · langchain 0. Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. 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. A few-shot prompt template can be constructed from either a set of examples, or Prompt Templates Prompt templates help to translate user input and parameters into instructions for a language model. For this reason, in the below example with an XML agent, we use the LangSmith is framework-agnostic — it can be used with or without LangChain's open source frameworks langchain and langgraph. Below is an example of how you can implement a multi-agent system for booking travel using handoffs: API Reference This section will cover building with LangChain Agents. This log can be used in Build resilient language agents as graphs. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). Build controllable agents with LangGraph, our low-level agent orchestration framework. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. In this comprehensive guide, we’ll In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. For more see the how-to guide for setting up LangSmith with LangChain or setting up LangSmith with LangGraph. The action consists of the name of the tool to execute and the input to pass to the tool. The main difference between the two is that our agent can query the database in a loop as many time as it needs to answer the question. Returning Structured Output This notebook covers how to have an agent return a structured output. For detailed documentation of all SQLDatabaseToolkit features and configurations head to the API reference. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. 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. Jan 11, 2024 · Discover the ultimate guide to LangChain agents. agents import load_tools Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. This flexibility enables the creation NOTE: for this example we will only show how to create an agent using OpenAI models, as local models runnable on consumer hardware are not reliable enough yet. How to add memory to chatbots A key feature of chatbots is their ability to use the content of previous conversational turns as context. The agent can store, retrieve, and use memories to enhance its interactions with users. Deploy and scale with LangGraph Platform, with APIs for state management, a visual studio for debugging, and multiple deployment options. LangChain is an open-source framework created to aid the development of applications leveraging the power of large language models (LLMs). Tools within the SQLDatabaseToolkit are designed to interact with a SQL database. , a tool to run). 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. A Python library for creating hierarchical multi-agent systems using LangGraph. csv. The agent returns the exchange rate between two currencies on a specified date. This repository contains a collection of apps powered by LangChain. langchain: 0. This guide covers a few strategies for getting structured outputs from a model. Common examples of these applications include: Question Answering over specific documents Documentation End-to-end Example: Question Answering over Notion Database 💬 Chatbots Documentation End-to-end Example: Chat-LangChain 🤖 Agents Documentation End-to-end Example: GPT+WolframAlpha 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. It can recover from errors by running a generated query, catching the traceback and regenerating it Deprecated since version 0. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. The log is used to pass along extra information about the action. The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. json. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. 3. from langchain_core. Why do LLMs need to use Tools? A big use case for LangChain is creating agents. Framework to build resilient language agents as graphs. It can recover from errors by running a generated query SQLDatabase Toolkit This will help you get started with the SQL Database toolkit. Classes In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. When the agent This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly. In Chains, a sequence of actions is hardcoded. The . Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). agent. Memory is needed to enable conversation. , runs the tool), and receives an observation. tools_renderer (Callable[[list[BaseTool]], str]) – This controls how the tools are Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) Text Splitter Playground - Play with various types of text splitting for RAG (Source code) Tweet Agents let us do just this. 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. g. Agents select and use Tools and Toolkits for actions. Finally, we will walk through how to construct a conversational retrieval agent from components. AgentAction # class langchain_core. 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). Embeddings The following example shows how to use the databricks-bge-large-en embedding model as an embeddings component in LangChain using the Foundation Models API. This tutorial, published following the release of LangChain 0. 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. Sep 18, 2024 · Let’s walk through a simple example of building a Langchain Agent that performs two tasks: retrieves information from Wikipedia and executes a Python function. LangChain comes with a number of built-in agents that are optimized for different use cases. We will also demonstrate how to use few-shot prompting in this context to improve performance. Apr 24, 2024 · This section will cover building with the legacy LangChain AgentExecutor. In chains, a sequence of actions is hardcoded (in code). Key concepts Tools are a way to encapsulate a function and its schema in a way that can be 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 How to create tools When constructing an agent, you will need to provide it with a list of Tools that it can use. Setup: LangSmith By definition, agents take a self-determined, input-dependent Build copilots that write first drafts for review, act on your behalf, or wait for approval before execution. This will assume knowledge of LLMs and retrieval so if you haven't already explored those sections, it is recommended you do so. These applications use a technique known as Retrieval Augmented Generation, or RAG. Load the LLM First, let's load the language model we're going to 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. note Mar 17, 2025 · In conclusion, LangChain’s tools and agents represent a significant leap forward in the development of AI applications. Contains previous agent actions and tool outputs as messages. LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. These are applications that can answer questions about specific source information. agent_toolkits. 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. Tools allow agents to interact with various resources and services like APIs May 9, 2025 · Conclusion LangChain provides a robust framework for building AI agents that combine the reasoning capabilities of LLMs with the functional capabilities of specialized tools. More complex modifications Overview The tool abstraction in LangChain associates a Python function with a schema that defines the function's name, description and expected arguments. See the multi-agent supervisor example for a full example of using Send() in handoffs. 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. LangChain has many other document loaders for other data sources, or you can create a custom document loader. We will first create it WITHOUT memory, but we will then show how to add memory in. In this example, we will use OpenAI Tool Calling to create this agent. Don’t delay; start leveraging LangChain to build innovative applications today. Quickstart In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. 2. By default, most of the agents return a single string. 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. These need to represented in a way that the language model can recognize them. The schemas for the agents themselves are defined in langchain. Contribute to johnsnowdies/langchain-sql-agent-example development by creating an account on GitHub. Here are the steps: Define and configure a model Define and use a tool (Optional) Store chat history (Optional) Customize the prompt template (Optional The prompt must have input keys: tools: contains descriptions and arguments for each tool. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. A common application is to enable agents to answer questions using data in a relational database, potentially in an create_json_agent # langchain_community. A good example of this is an agent tasked with doing question-answering over some sources. Agents use language models to choose a sequence of actions to take. What Is LangChain? Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. In Build an Extraction Chain In this tutorial, we will use tool-calling features of chat models to extract structured information from unstructured text. One common use-case is extracting data from text to insert into a database or use with some other downstream system. AgentAction [source] # Bases: Serializable Represents a request to execute an action by an agent. js to build stateful agents with first-class streaming and human-in-the-loop 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). ⚠️ Security note ⚠️ Feb 16, 2025 · Agents in LangChain are advanced components that enable AI models to decide when and how to use tools dynamically. Chains are great when we know the specific sequence of tool usage needed for any user input. If you are using either of these, you can enable LangSmith tracing with a single environment variable. agent_scratchpad: must be a MessagesPlaceholder. When the agent reaches a stopping condition, it returns a final return value. These agents, rather than following a static sequence, tailor their responses by intelligently selecting and chaining tools based on real-time input. Building an agent from a runnable usually involves a few things: Data processing for the intermediate steps (agent_scratchpad). The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. 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. 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. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. \nYour goal is to return a final answer by interacting with the JSON. Follow their code on GitHub. prompts import ChatPromptTemplate system_message = """ Given an input question, create a syntactically correct {dialect} query to run to help find the answer. wccdu cfa imqple cqfo lqzv jaz kkxfgvi gtcgfw xkwqp frmi