Agents and toolkits | 🦜️🔗 Langchain

📄️ AINetwork

AI Network is a layer 1 blockchain designed to accommodate large-scale AI models, utilizing a decentralized GPU network powered by the $AIN token, enriching AI-driven NFTs (AINFTs).

📄️ Airbyte Question Answering

This notebook shows how to do question answering over structured data, in this case using the AirbyteStripeLoader.

📄️ Amadeus

This notebook walks you through connecting LangChain to the Amadeus travel information API

📄️ Azure Cognitive Services

This toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.

📄️ CSV

This notebook shows how to use agents to interact with data in CSV format. It is mostly optimized for question answering.

📄️ Document Comparison

This notebook shows how to use an agent to compare two documents.

📄️ Github

The Github toolkit contains tools that enable an LLM agent to interact with a github repository.

📄️ Gitlab

The Gitlab toolkit contains tools that enable an LLM agent to interact with a gitlab repository.

📄️ Gmail

This notebook walks through connecting a LangChain email to the Gmail API.

📄️ Google Drive tool

This notebook walks through connecting a LangChain to the Google Drive API.

📄️ Jira

This notebook goes over how to use the Jira toolkit.

📄️ JSON

This notebook showcases an agent interacting with large JSON/dict objects.

📄️ MultiOn

This notebook walks you through connecting LangChain to the MultiOn Client in your browser

📄️ Office365

This notebook walks through connecting LangChain to Office365 email and calendar.

📄️ OpenAPI

We can construct agents to consume arbitrary APIs, here APIs conformant to the OpenAPI/Swagger specification.

📄️ Natural Language APIs

Natural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints.

📄️ Pandas Dataframe

This notebook shows how to use agents to interact with a Pandas DataFrame. It is mostly optimized for question answering.

This toolkit is used to interact with the browser. While other tools (like the Requests tools) are fine for static sites, PlayWright Browser toolkits let your agent navigate the web and interact with dynamically rendered sites.

📄️ PowerBI Dataset

This notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors.

📄️ Python

This notebook showcases an agent designed to write and execute Python code to answer a question.

📄️ Spark Dataframe

This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.

📄️ Spark SQL

This notebook shows how to use agents to interact with Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.

📄️ SQL Database

This notebook showcases an agent designed to interact with a SQL databases.

📄️ Vectorstore

This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.

📄️ Xorbits

This notebook shows how to use agents to interact with Xorbits Pandas dataframe and Xorbits Numpy ndarray. It is mostly optimized for question answering.