Langchain Csv Agent Example, Integrate with the CSV document loader using LangChain Python. A collection of projects showcasing RAG, agents, workflows, and other AI use cases - Arindam200/awesome-ai-apps This is a small natural-language-to-SQL data agent inspired by the architecture of eosho/langchain_data_agent, but implemented from scratch for this cookbook. This tutorial covers how to create an agent that performs analysis on the Pandas DataFrame loaded from CSV or Excel files. If a user query retrieves that chunk, the model may output JSON instead of a natural-language answer. It is mostly optimized for question answering. The agent generates Pandas queries to analyze the dataset. . See the docs for conceptual guides, tutorials, and examples on using Agents. LangChain Samples is a collection of code examples, cookbooks, reference implementations, and workshop materials created by customer facing teams at LangChain. With this agent, we’ll automate typical exploratory data analysis (EDA) tasks as displaying columns, detecting missing values (NaNs) and retrieving descriptive statistics. For detailed information about the underlying agent implementation, prompt strategies, and agent types, see Pandas DataFrame Agent. This page contains reference documentation for Agents. It keeps the useful shape: Local CSV sample data loaded into an in-memory SQLite Repository files navigation prompt-baker Genetic optimization for prompt + model combinations on CSV benchmarks. Feb 12, 2026 · Home / Blog / AI Frameworks & Technical Infrastructure / LangChain (Setup, Tools, Agents, Memory) / LangChain Document Loaders: Co… LangChain Document Loaders: Complete Guide to Loading Files + Code Examples 2025 Explore how document loaders streamline data processing from various formats, enhancing efficiency and accuracy for AI applications. Jan 12, 2024 · In this article, I have explained what is LangChain Agent, what it does, and how it works using the CSV Agent as an example. Use cautiously. I have also explained the different between the two agent types — ZERO_SHOT_REACT_DESCRIPTION and OPENAI_FUNCTIONS and they affect the responses returned by the model. This notebook shows how to use agents to interact with a csv. This article walks through practical scenarios, from using existing agents and composing multi-agent graphs to tool-enabled workflows, human-in-the-loop approvals, and tracing. For example, the blog post indexed in this tutorial contains text describing an Auto-GPT JSON response format. Jan 13, 2026 · This document covers the create_csv_agent function, its CSV loading mechanics, and configuration options. The goal is a compact PoC, not a full data platform. Nov 7, 2024 · When given a CSV file and a language model, it creates a framework where users can query the data, and the agent will parse the query, access the CSV data, and return the relevant Sep 9, 2025 · In this article, we’ll use LangChain and Python to build our own CSV sanity check agent. You can still define the available toolset and guidelines for how agents behave. To get started with agents, see the quickstart or read more about how they work in LangChain. LangChain provides a prebuilt agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications. These resources are designed purely for educational and demonstration purposes, helping developers explore patterns, integrations, and best Mar 24, 2026 · Use the langchain-azure-ai package to connect LangGraph and LangChain applications to Foundry Agent Service. NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. 3 days ago · FarmWise （慧农通---智慧农业播RAG助手） 基于 LangChain , ReAct , RAG 架构的农业专家智能客服系统，支持 RAG 知识库检索、实时天气适配、地理位置感知、个人使用报告生成，通过 FastAPI + 原生 HTML/JS 前端提供流式对话体验。 Community-maintained LangChain integrations. Agents have more autonomy than workflows, and can make decisions about the tools they use and how to solve problems. You inject chat completion functions (any API, local model, LangChain agent, or heuristic), supply pools of system and user prompt templates, and search for high-scoring candidates using classification or generation metrics. Contribute to langchain-ai/langchain-community development by creating an account on GitHub. Build completely custom agents and applications powered by LLMs in under 10 lines of code, with integrations for OpenAI, Anthropic, Google, and more. 2x9aly0st0r1bo2v29ibxhkv5w93w21im