Langchain agent types github. You can run these files individually to interact with the respective agents. agents import initialize_agent from langchain. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. log_model and log the model. py, math_agent. agent_types import AgentType Dec 21, 2023 路 Hello Everyone, I am using LLAMA 2 70 B and Langchain . This agent uses a search tool to look up answers to the simpler questions in order to answer the original complex question. agents. 馃馃敆 Build context-aware reasoning applications. Dec 9, 2024 路 An agent that breaks down a complex question into a series of simpler questions. However, it is much more challenging for LLMs to do this, so some agent types do not support this. Here's a brief overview: ZERO_SHOT_REACT_DESCRIPTION: This is a zero-shot agent that performs a reasoning step before acting. Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production. Each agent is implemented in a separate Python file (music_agent. Open Agent Platform is a no-code agent building platform. 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. agent_types. These agents can be connected to a wide range of tools, RAG servers, and even other agents through an Agent Supervisor! Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. This document explains the purpose of the protocol and makes the case for each of the endpoints in the spec. I found the below Jun 17, 2025 路 LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. py, finance_agent. For details, refer to the LangGraph documentation as well as guides for Migrating from AgentExecutor and LangGraph’s Pre-built ReAct agent. This repository is a practical resource for learning, experimenting, and creating LLM-powered applications using LangChain. 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 . note Apr 4, 2023 路 when I follow the guide of agent part to run the code below: from langchain. langchain. It's suitable for scenarios where an immediate response is required without prior training. To use the Agent Inbox, you'll have to use the interrupt function, instead of raising a NodeInterrupt exception in your codebase. com/api_reference/langchain/agents/langchain. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. To read more about how the interrupt function works, see the LangGraph documentation: conceptual guide how-to guide (TypeScript docs coming soon, but the concepts & implementation are the same). The first issue was that each one expected a different type of input. agents import load_tools from langchain. I have some custom tools and created a chatbot. py). An agent that breaks down a complex question into a series of simpler questions. Jul 20, 2023 路 I just realized that using routing with different type of agents or chains is simply impossible (at least for now). AgentType. It works fine . I want to use mlflow. html Checklist I added a very descriptive title to this issue. Nov 4, 2023 路 In the LangChain framework, each AgentType is designed for different scenarios. Feb 16, 2025 路 Types of LangChain Agents Reactive Agents — Select and execute tools based on user input without long-term memory. Contribute to langchain-ai/langchain development by creating an account on GitHub. LangChain Agents and Workflows 馃殌 A hands-on collection of projects demonstrating the power of the LangChain framework to build AI-driven workflows and intelligent agents. Having an LLM call multiple tools at the same time can greatly speed up agents whether there are tasks that are assisted by doing so. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. 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. Was trying to create an agent that has 2 routes (The first one being an LLMChain and the second being a ConversationalRelationChain). Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. URL https://python. tqpl osmidok utgu skvxdwg cdsg ygop fxwdy sisu stae mgompp