Langchain agents documentation template github. py, demonstrates a flexible ReAct agent that iteratively .

Langchain agents documentation template github. agents import AgentExecutor, create_react_agent. This repository contains a comprehensive, project-based tutorial that guides you through building sophisticated chatbots and AI applications using LangChain. In this case, we save all memories scoped to a configurable user_id, which lets the bot learn a user's preferences across conversational threads. 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 You can just invoke it with an empty list (default) to index sample documents from LangChain and LangGraph documentation. combine_documents import create_stuff_documents_chain. They are all in a standard format which make it easy to deploy them with LangServe. The template is organized to be easily 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. py: An agent that replicates the MRKL demo (View the app) minimal_agent. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. py that implement a retrieval-based question answering system. , runs the tool), and receives an observation. You'll know that the indexing is complete when the indexer "delete"'s the content from its graph memory (since it's been persisted in your configured storage provider). These templates serve as a set of reference architectures for a wide variety of popular LLM use cases. ChatOpenAI (View the app) basic_memory. It's designed with simplicity in mind, making it accessible to users without technical expertise, while still offering advanced capabilities for developers. This repository contains reference implementations of various LangChain agents as Streamlit apps including: basic_streaming. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. py: Simple streaming app with langchain. js + Next. langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture. The core logic, defined in src/react_agent/graph. py: Simple app using StreamlitChatMessageHistory for LLM conversation memory (View the app) mrkl_demo. Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. 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. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. This is a simple way to let an agent persist important information to reuse later. , to populate a database or spreadsheet) from open-ended research (e. chat_models. LangGraph Data Enrichment Template Producing structured results (e. vectorstores import Chroma. g. Jun 17, 2025 · In this tutorial we will build an agent that can interact with a search engine. This is not possible if you want to go to production, because it requires every user to have their own LangSmith API key, and set the LangGraph configuration themselves. LangSmith documentation is hosted on a separate site. Here, we provide a general template for this kind of "data enrichment agent" agent using LangGraph in LangGraph Studio. js starter app. Multi-Agent Architectures with Langchain This project explores multiple multi-agent architectures using Langchain (LangGraph), focusing on agent collaboration to solve complex problems. The code snippet below represents a fully functional agent that uses an LLM to decide which tools to use. You will learn everything from the fundamentals of chat models to advanced concepts like Retrieval-Augmented Generation (RAG), agents, and custom tools. A basic agent works in the following manner: Given a prompt an agent uses an LLM to request an action to take (e. You can peruse LangSmith how-to guides here, but we'll highlight a few sections that are particularly relevant to LangChain below: Evaluation By default, the Agent Chat UI is setup for local development, and connects to your LangGraph server directly from the client. . chains. Agents use language models to choose a sequence of actions to take. js template - template LangChain. chains import create_history_aware_retriever, create_retrieval_chain. The agent executes the action (e. LangGraph Retrieval Chat Bot Template This is a starter project to help you get started with developing a retrieval agent using LangGraph in LangGraph Studio. It contains example graphs exported from src/retrieval_agent/graph. 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 Contribute to langchain-ai/rag-research-agent-template development by creating an account on GitHub. py, demonstrates a flexible ReAct agent that iteratively This template scaffolds a LangChain. from langchain. LangGraph ReAct Memory Agent This repo provides a simple example of a ReAct-style agent with a tool to save memories. Jun 20, 2025 · from langchain. from langchain_community. It showcases how to use and combine LangChain modules for several use cases. , web research) is a common use case that LLM-powered agents are well-suited to handle. It comes with pre-configured setups for chains, agents, and utility functions, enabling you to focus on developing your application rather than setting up the basics. LangChain + Next. It is equipped with a generic search tool. , a tool to run). Oct 31, 2023 · LangChain Templates are the easiest and fastest way to build a production-ready LLM application. py: A Examples include langchain_openai and langchain_anthropic. This template serves as a starter kit for creating applications using the LangChain framework. This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. psio vpzxpg ecjhb vues fbpnc phmhvkj kksu ygu ujeezqffq tdosc

26th Apr 2024