Langgraph csv agent github. 🦜🔗 Build context-aware reasoning applications.
Langgraph csv agent github. It includes a LangGraph agent, a FastAPI service to serve it, a client to interact with the service, and a Streamlit app that uses the client to provide a chat interface. It showcases the seamless integration of tabular and textual data extracted from PDFs into a unified query system . The app reads the CSV file and processes the data. 130. Separate from the LangChain package, LangGraph’s core design philosophy is to help developers add better precision and control into agent workflows, suitable for the complexity of real-world systems. GitHub - lenaar/financial-ai-agent: This repository contains a sophisticated AI agent built with LangGraph and LangChain that automates financial analysis workflows. chat_models. LangGraph's main use is for adding cycles to LLM applications LangGraph创建agent的中文文档. Contribute to langchain-ai/langgraph development by creating an account on GitHub. I used the GitHub search to find a similar question and Pass the summary, previous_csv, and current_csv stored in our LangGraph state to the LLM, and the previous_csv and current_csv to the Riza function call. This tool takes a user-uploaded CSV and answers natural language questions by generating and executing SQL queries — all within a few seconds. By @joaomdmoura. We have Amazon Bedrock Custom LangChain Agent Create a custom LangChain agent dubbed "Agent AWS" that queries the AWS Well-Architected Framework and deploys Lambda functions, all backed by Amazon Bedrock and housed in a Streamlit chatbot. This is a ReAct agent which uses the PythonREPLTool. Save the chart image to a local file. A fullstack AI agent platform built with React and LangGraph, featuring multiple specialized agents, real-time activity tracking, and MCP tool integrations for advanced conversational AI workflows Oct 20, 2024 · from langgraph. langgraph-bigtool langgraph-bigtool is a Python library for creating LangGraph agents that can access large numbers of tools. message import MessagesState def call_model (state: MessagesState): # add any logic to customize model system message etc here response = model. Leveraging LangChain, OpenAI GPT, and LangGraph, this tool streamlines hypothesis generation, data analysis, visualization, and report writing. Contribute to AlexLIAOPOLY/LangGraph_Agent development by creating an account on GitHub. Feb 21, 2025 · Let's walk through how to develop a multiagent workflow in LangGraph using the DeepSeek R1 model. This Repository will guide you in building an Agentic RAG application using LangGraph and Qdrant. LangGraph development by creating an account on GitHub. My objective is to develop an Agent using Langchain, that can take actions on inputs from LLM conversations, and execute various scripts or one-off s Feb 19, 2025 · This guide will walk you through building an AI agent with LangGraph and highlight the LangGraph-AI-Agent repository by hulk-pham—a project that demonstrates advanced multi-agent conversational systems, dynamic workflow orchestration, custom agent behaviors, and robust state management. May 16, 2025 · About the CSV Agent client: This is a conversational agent set using LangGraph create_react_agent that can store the history of messages in its short term memory as a checkpointer and makes Apr 11, 2025 · Analyze the responses from sql_agent and propose a better query or changes in database schema to improve the performance of the query if needed (Do it yourself). The agent processes financial data from CSV files, performs competitor analysis, and generates detailed reports with interactive feedback loops. agents import create_pandas_dataframe_agent import pandas as pd # Load your DataFrame df = pd. py: An agent that replicates the MRKL demo (View the app) minimal_agent. EduTrack LangGraph is an AI-powered assistant built with LangGraph, LangChain, and Streamlit. It leverages LangGraph's long-term memory store to allow an agent to search for and retrieve relevant tools for a given problem. These tools enable the creation of powerful AI-driven conversational agents with flexible and scalable architectures. Sep 6, 2024 · LangGraphのGitHubリポジトリには、 examples として、LangGraphを使ったさまざまな実装が共有されています。 このexamplesの中から Build a Customer Support Bot のnotebookを参考に、エージェントの作り方を学びたいと思います。 本notebookはPart1からPart4で構成されています。 すべて航空会社のカスタマーサポート LangGraph Multi-Agent Chain: Question Agent analyzes your query. Sep 12, 2024 · GitHub 仓库 托管应用 让我们来探索一个令人兴奋的项目，该项目利用 LangGraph Cloud 的流式 API 来创建一个数据可视化 Agent。 您可以上传 SQLite 数据库或 CSV 文件，提出关于您数据的问题，Agent 将生成适当的可视化图表。 这篇博文简要介绍了 Agent 的工作流程和主要 This project is an SQL Query Assistant that automates the process of generating, executing, and explaining SQL queries using a combination of a Graph-based Workflow and a Large Language Model (LLM). graph import StateGraph, START from langgraph. agents. Contribute to JoshiSneh/Data-Visualization-Python-Langgraph development by creating an account on GitHub. Adaptive RAG (paper). Web Search Agent: Expands the research context through online searches. A full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit. An interactive agent built using LangGraph, powered by the Mistral-3. py: Simple app using StreamlitChatMessageHistory for LLM conversation memory (View the app) mrkl_demo. This repository contains an 'agent' which can take in a URL, and generate a Twitter & LinkedIn post based on the content of the URL. Langraph Chat model based web scrape tool. Result Display: The answer and its page number are shown in the Streamlit interface. It's designed with simplicity in mind, making it accessible to users without technical expertise, while still offering advanced capabilities for developers. The end product is an end-to-end chatbot, designed to perform these tasks, with LangSmith used to monitor the performance of the agents. The project utilizes a Mar 7, 2024 · create_csv_agent is a convenience function that loads a CSV into a pandas dataframe and calls create_pandas_dataframe_agent. It leverages langgraph for state management and OpenAI's GPT for intelligent query generation and response formatting. Streamlit Text to SQL Agentic ChatBot app built with langgraph workflow : Workflow : LangGraph Workflow with text-to-query, sqlite, and memory & session management Inference & LLM : Groq Inference, Model : llama3. 2-24B model via OpenRouter. I used the GitHub search to find a similar question and Jan 30, 2024 · Checked other resources I added a very descriptive title to this question. This project provides both a Python API and a RESTful service for document analysis create_csv_agent # langchain_experimental. These methods calculate specific statistics related to the multi-agent infrastructure's performance and enable visualizations of the process behavior and execution flow. Apr 25, 2025 · Set Up We’ll be building a simple Agent to demonstrate the end-to-end process. Integrates with OpenAI, Anthropic, and Google AI models. 2 3b AI-Driven Research Assistant: An advanced multi-agent system for automating complex research processes. In Here we will build reliable RAG agents using LangGraph, Groq-Llama-3 and Chroma, We will combine the below concepts to build the RAG Agent. An agent is a system driven by a language model that makes The purpose of this repository is to demonstrate how LangGraph can be used to build a stateless multi-agent workflow to serve as an assistant for data analysis. agent_toolkits. An agent is a custom Build resilient language agents as graphs. You can use any LLM of your choice. To tackle this problem, we’ve built LangGraph — a framework for building agent and multi-agent applications. - aimped-ai/ai-data-analysis-MultiAgent Jun 17, 2024 · Is it possible to get an local pandas DataFrame in agentic workflow and ask an agent to analyze the structured data using Python (as suggested in this link)? I love this concept and am trying to expand it to real-life examples by adding more agents. The core logic, defined in src/react_agent/graph. Built with LangGraph, LangChain, and Streamlit. Mar 6, 2024 · Here's an example of how you can do this: from langchain_openai import ChatOpenAI from langchain_experimental. Jan 14, 2025 · Leverage LangGraph to orchestrate a powerful Retrieval-Augmented Generation workflow tablegpt-agent is a pre-built agent for TableGPT2 (huggingface), a series of LLMs for table-based question answering. Contribute to selfepc/langgraph-agent development by creating an account on GitHub. It can: Contribute to PoorvikaGirishBabu/Creating-a-multiagent-system-with-Langgraph development by creating an account on GitHub. This project demonstrates a fullstack application using a React frontend and a LangGraph-powered backend agent. The The workflow is orchestrated using LangGraph, which provides a framework for easily building complex AI agents, a streaming API for real-time updates, and a visual studio for monitoring and experimenting with the agent's behavior. Sep 24, 2024 · Hello everyone , I'm working on a project that is built on Langgraph's multi-agent system ( Hierarchical architecture ) . It enables the construction of cyclical graphs, often needed for agent runtimes, and extends the LangChain Expression Language to coordinate multiple chains or actors across multiple steps. It uses a human-in-the-loop (HITL) flow to handle authentication with different social media platforms, and to allow the user to make changes, or accept/reject the AI PDF Chatbot & Agent Powered by LangChain and LangGraph This monorepo is a customizable template example of an AI chatbot agent that "ingests" PDF documents, stores embeddings in a vector database (Supabase), and then answers user queries using OpenAI (or another LLM provider) utilising LangChain and LangGraph as orchestration frameworks. There are a few subtleties that I'd like to raise to the developers, so to follow the principles of the library. This workflow leverages the pybaseball Python library to extract data which is then used for analysis based on the user's request. Multi-Agent Data Analytics System A robust, intelligent multi-agent system for comprehensive data analytics with context-aware query routing, dynamic chart generation, and flexible data exploration. io LangGraph is a library built on top of LangChain, designed for creating stateful, multi-agent applications with LLMs (large language models). - agno-agi/agno Aug 12, 2024 · Suggestions on how to go about dynamic code generation using multi-agent approach A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Around the LangGraph agent, the workflow uses a SQLite Server that supports file (SQLite and CSV) uploads under 1MB and a front-end that has prebuilt graph templates The workflow is orchestrated using LangGraph, which provides a framework for easily building complex AI agents, a streaming API for real-time updates, and a visual studio for monitoring and experimenting with the agent's behavior. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. This guide explains how to set up PostgreSQL, create a project directory, build the database tables and import data, and run a LangGraph-based text-to-SQL AI agent. ChatOpenAI (View the app) basic_memory. This is a collection of examples of different ways to use the crewAI framework to automate the processes. We're going to develop RAG and tabular data agents. Building more sophisticated AI agents is a topic better suited for a dedicated post. crewAI is designed to facilitate the collaboration of role-playing AI agents. The system is designed to extract information from documents, build a knowledge graph, and provide answers to user queries. Features: Jan 8, 2024 · LangGraph-financial-agent. 5-turbo", temperature=0) 🚀 Overview AI Data Analyst Agent is an intelligent web app that transforms your CSV data into actionable insights using Streamlit, LangGraph, and LLMs. This repository contains reference implementations of various LangChain agents as Streamlit apps including: basic_streaming. Build resilient language agents as graphs. I'd appreciate any advice and sample code. Data structures and settings are Jul 6, 2024 · This discussion is to develop a mapping between libraries for the example of re-implementing the create_pandas_dataframe_agent in LangGraph. The The plan for this project is to iteratively improve the Hospital System Chatbot over time as new libraries, techniques, and models emerge in the RAG and Generative AI space. When you have all answers, analyze and generate a plan to improve the query performance, returning the planned improvements. csv") # Initialize the ChatOpenAI model llm = ChatOpenAI (model="gpt-3. An AI SQL agent is created using is created using Langchain and LangGraph which takes csv or excel file and you can any question about your file from it first it convert it into SQL query then it The purpose of this repository is to demonstrate how LangGraph can be used to build a stateless multi-agent workflow to serve as an assistant for data analysis. path (Union[str, IOBase 🦜🔗 Build context-aware reasoning applications. GPT-4o Agent composes a final, human-readable response. Each record consists of one or more fields, separated by commas. The agent uses a Tavily-based language model client to convert natural language queries into SQL queries, executes them on a PostgreSQL database, and returns the results. csv. Multi-Agent Research System (using LangGraph) Document Selection: Provides access to parsed documents for research purposes. It dynamically processes student queries, routes them to intelligent agents (Academic, Career, Wellness, and Performance), and delivers personalized recommendations by grounding responses in structured CSV datasets. A powerful document analysis and processing agent built with LangGraph, designed to work with Google Cloud Storage and various document formats including PDF, CSV, and text files. Perfect for researchers and data scientists seeking to enhance their workflow and productivity. A comprehensive toolkit for building, deploying, and managing AI agents using LangGraph, FastAPI, and Streamlit. Nov 7, 2024 · The create_csv_agent function in LangChain works by chaining several layers of agents under the hood to interpret and execute natural language queries on a CSV file. 🚀 Built for fast querying, natural interaction, and learning how AI can interact with structured data. Apr 2, 2024 · I am using MacOS, and installed Ollama locally. base. Contribute to dnayal/langgraph_quickstart development by creating an account on GitHub. Each line of the file is a data record. GitHub Gist: instantly share code, notes, and snippets. Contribute to nvns10/langgraph_examples development by creating an account on GitHub. The CSV agent then uses tools to find solutions to your questions and generates an appropriate response with the help of a LLM. The agent architecture is as follows: After the execute_sql_query node is executed, the data is saved as a CSV on the host machine. The agent is designed to perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search, reflecting on the results to Jun 10, 2025 · Supervisor型Multi Agentシステムとは、Supervisorと呼ばれる全体を統制するAgentがツールコール対応の各LLM Agentと連携して、どのAgentをいつ呼び出すか、またそれらのAgentに渡す引数を決定するMulti Agent構造です。 langgraph-supervisorでMulti Agentシステムを構築 Mar 9, 2011 · About AI Agent RAG & SQL Chatbot enables natural language interaction with SQL databases, CSV files, and unstructured data (PDFs, text, vector DBs) using LLMs, LangChain, LangGraph, and LangSmith for retrieval and response generation, accessible via a Gradio UI, with LangSmith monitoring. 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. Around the LangGraph agent, the workflow uses a SQLite Server that supports file (SQLite and CSV) uploads under 1MB and a front-end that has prebuilt graph templates Build resilient language agents as graphs. github. To start with, create a following project structure and open langgraph_deployment directory in your favorite code editor. Here we are just using two Agents one for document retrieval and the other one for wikipedia search 🚀 Cross-Sell/Upsell Recommendation LangGraph Agent A modular LangGraph-based AI agent that delivers cross-sell and upsell recommendations powered by customer insights and purchase behavior analysis. This project is a multi-agent system powered by LangGraph, designed to orchestrate multiple agents exposed via APIs. csv_agent # Functionslatest This project demonstrates how to build a powerful multimodal agent for document analysis using Docling for PDF extraction and LangChain for creating AI chains and agents. Includes CLI and FastAPI server for quick deployment. We have implemented the concept to build a Router for routing questions to different retrieval approaches Corrective RAG (paper). ipynb. Mar 4, 2025 · Agentsを利用して、DBに保存された内容から、LLMがRAGで質問に回答する では、実際にAgentを実装します。 Agentといっても、主に利用するのはTool Callingです。 RAGの機能をToolとして設定して、あとはLLMがToolを使うか否かを判断すると言う、よくあるTool Callingの形 This project implements a Multi-Agent Reinforcement Learning Answer Graph (RAG) system along with workflow automation for processing PDF and CSV files. The app uses Streamlit to create the graphical user interface (GUI) and uses Langchain to interact with the LLM. Create complex LLM agent graphs without coding, convert simple spreadsheets into powerful AI agents, and orchestrate multi-agent systems with ease. What is LangGraph? Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning. I searched the LangChain documentation with the integrated search. My multi-agent system is derived from here : https://langchain-ai. This project utilizes the LangChain and LangGraph framework to create a Multi-Agent enabled conversational interface for performing various tasks such as analyzing CSV data and extracting information from resumes or portfolios. The key frameworks used in this project include OpenAI, LangChain, LangGraph, LangSmith, and Gradio. This repository demonstrates how to build chatbots using the langgraph and langchain ecosystems. read_csv ("your_data. It stands out by supporting cycles for agentic architectures, offering fine-grained control over application flow and state, and including built-in persistence for memory and human-in-the-loop features. Contribute to AI-App/LangChain-AI. This agent is built on top of the Langgraph library and provides a user-friendly interface for interacting with TableGPT2. Mar 9, 2024 · Checked other resources I added a very descriptive title to this question. It utilizes OpenAI LLMs alongside with Langchain Agents in order to answer your questions. This assistant logs tool usage, performs in-depth analysis of usage data, and provides both a chatbot and analytics interface through Streamlit. py: A Sep 25, 2024 · Checked other resources I added a very descriptive title to this question. py, demonstrates a flexible ReAct agent that iteratively It highlights the use of SQL agents to efficiently query large databases. Oct 11, 2024 · With the advent of tools like Langgraph and LLMs (Large Language Models), it’s now possible to build AI agents that can run complex machine learning models and provide valuable insights. Data visualization using Langgraph. If external_tools is passed as part of the **kwargs argument, it would be passed along to the create_pandas_dataframe_agent function, but the provided context does not show how create_pandas_dataframe_agent handles external Jul 22, 2024 · Advanced AI-Driven Data Analysis System: A LangGraph Implementation Project Overview I've developed a sophisticated data analysis system that leverages the power of LangGraph, showcasing its capabi Build resilient language agents as graphs. It provides a production-ready framework for creating conversational AI agents with features like multi-provider LLM support, streaming responses, observability, and memory management. 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. Retriever Agent pulls relevant chunks from the PDF. Built as part of the Zeeproc Gen AI Engineer Hiring Assignment, this solution combines intelligent data processing, modular agents, and API integration for practical business use. Mar 16, 2024 · LangGraph, developed by LangChain, is a pioneering framework designed to facilitate the creation and management of AI agents. RAG Agent: Answers user queries using Retrieval-Augmented Generation with Pinecone. In this article, we’ll explore how I created a Multi Agent System to run a linear regression model using Langgraph and the Llama3. Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. invoke (state ["messages"]) Once in csv format, the data can be analyzed using methods from the py4pm library. Contribute to lloydchang/langchain-ai-langgraph development by creating an account on GitHub. Contribute to jurnea/LangGraph-Chinese development by creating an account on GitHub. It employs OpenAI's language models and tools to enable natural language interactions with the system. The system uses natural language input from users to determine which agents to invoke, route data accordingly, and produce an appropriate response — all within a dynamic, graph-based workflow. lenaar / financial-ai-agent Public Notifications You must be signed in to change Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. py: Simple streaming app with langchain. Contribute to JaiBhagat/LangGraph-Multi-Agent-Chatbot development by creating an account on GitHub. Project Overview: This With database access and coding capability. Oct 2, 2024 · Create the LangGraph Agent: Use the create_react_agent function to set up the agent with the defined tools. About LangGraph is a library for building stateful, multi-agent workflows with LLMs. Note: All examples have been standardized to use CrewAI version 0. Sep 12, 2024 · Let's explore an exciting project that leverages LangGraph Cloud's streaming API to create a data visualization agent. Parameters: llm (LanguageModelLike) – Language model to use for the agent. You can upload an SQLite database or CSV file, ask questions about your data, and the agent will generate appropriate visualizations. So when the agent executes the code, it will create a docker container, execute the code, and then remove the container. I used the GitHub search to find a similar question and A powerful AI assistant built using LangGraph and Groq LLM, capable of answering user queries and intelligently invoking multiple tools like Wikipedia, Arxiv, PDF retrieval, web search, joke generation, and CSV data analytics. graph. Contribute to langchain-ai/langchain development by creating an account on GitHub. Jan 26, 2024 · The function primarily focuses on creating a CSV agent by loading data into a pandas DataFrame and using a pandas agent. 2:1b model. This ensures that the host machine is safe from arbitrary code from the agent. You should get comparable answers with either. LangChain is used for managing the LLM interface, while This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. Contribute to jayaraj/universal-scraper-langgraph development by creating an account on GitHub. Here are a few features currently in the backlog: Memory with Redis Hybrid structured and unstructured RAG Multi-modal RAG An email tool Data visualizations Stateful agents with LangGraph Terraform to provision cloud GitHub - jwwelbor/AgentMap: AgentMap: Build and deploy LangGraph workflows from CSV files. Arxiv Agent: Retrieves relevant research papers. Jul 22, 2024 · About Data Visualization using LangGraph Data visualization using LangGraph involves orchestrating a multi-agent system to analyze data and create visual representations efficiently. Set Up the Workflow: Define a StateGraph to manage the workflow, ensuring that the agent processes the input and executes the necessary tools. 0 for consistency and compatibility. Jan 13, 2025 · In this section, we create a ReAct-style agent that uses LangGraph to decide when to invoke tools like supplier-count and supplier-list. Page Agent finds the page number of the source. hkqudw bkibtu xmfk hvdrg sjql bklca dqalrif jmmfz bpbmki kaztgq