node-sdk

The ChatBotKit Node SDK offers a JavaScript-based platform for effortlessly building conversational AI bots and agents.

Stars: 52

Visit

The ChatBotKit Node SDK is a JavaScript-based platform for building conversational AI bots and agents. It offers easy setup, serverless compatibility, modern framework support, customizability, and multi-platform deployment. With capabilities like multi-modal and multi-language support, conversation management, chat history review, custom datasets, and various integrations, this SDK enables users to create advanced chatbots for websites, mobile apps, and messaging platforms.

README:

    .d8888b.  888888b.   888    d8P
   d88P  Y88b 888  "88b  888   d8P
   888    888 888  .88P  888  d8P
   888        8888888K.  888d88K
   888        888  "Y88b 8888888b
   888    888 888    888 888  Y88b
   Y88b  d88P 888   d88P 888   Y88b
    "Y8888P"  8888888P"  888    Y88b .ai

ChatBotKit Node SDK

Welcome to the ChatBotKit Node SDK. This SDK offers a JavaScript-based platform for effortlessly building conversational AI bots and agents. With ChatBotKit, you can swiftly develop and deploy AI bots capable of natural language interactions.

This is a meta repository for the ChatBotKit Node SDK. It contains the SDK packages for a number of popular platforms and frameworks such as React, Next.js, NextAuth and more.

Main Packages

The ChatBotKit Node SDK is comprised of the following packages:

Package	Version	Description
@chatbotkit/cli		The ChatBotKit CLI.
@chatbotkit/sdk		The ChatBotKit API SDK.
@chatbotkit/react		The ChatBotKit React SDK.
@chatbotkit/next		The ChatBotKit Next.js SDK.
@chatbotkit/nextauth		The ChatBotKit NextAuth.js SDK.
@chatbotkit/fetch		The ChatBotKit isometric fetch implementation.

Other Tools

This repository also contains the following tools:

Package	Version	Description
create-cbk-app		A quick tool to create a new CBK application.

SDK Features

Easy Setup: Quick and straightforward installation process.
Serverless Compatibility: Works seamlessly with modern runtime environments like Serverless, Edge, Vercel, Netlify, Cloudflare Workers, Deno, AWS Lambda, and more.
Modern Framework Support: Built-in support for CommonJS, ECMAScript Modules, async/await, streams, etc.
Customizability: Tailor the chatbot’s behavior and responses to fit specific use cases.
Multi-Platform: Deploy chatbots on websites, mobile apps, and messaging platforms like Slack, Discord, and WhatsApp.
Multi-Model: Support for a wide range of language models, including GPT-3, GPT-4, Claude, and more.

ChatBotKit Capabilities

🔄 Multi-modal Support: Support various language and image models from all vendors such as OpenAI, Anthropic, Mistral, AWS, Google and others.
🌍 Multi-language Support: Allowing for easy customization and use in diverse linguistic contexts.
💬 Conversation Management: Manage complex conversation flaws with ease.
🗨 Chat History: Review and reference past conversations.
💾 Custom Datasets: Organize data for bot responses.
💡 Custom Skillset: Equip chatbots with unique abilities like image generation or web fetching.
📄 Document File Importing: Import various document file types into chatbot datasets.
🎵 Media File Importing: Import a range of media file formats into chatbot datasets.
🌐 Widget Integration: Seamlessly embed chatbots on websites with customizable options.
💬 Slack, Discord, WhatsApp Bot Integrations: Easy integration with popular messaging platforms.
🗺 Sitemap Integration: Ingest website content into a searchable knowledge base.
🎥 Streaming: Enable/disable streaming capabilities.
🔒 Data Security: Robust measures to protect user data.
🕵 Privacy Focus: Strong privacy controls to ensure responsible data handling.
🚫 Content Moderation: Automatic scanning and flagging of abusive content.
💵 Simple Pricing: Transparent and straightforward pricing.

Getting Started

Follow these steps to start with ChatBotKit:

Installation:
```
npm install @chatbotkit/sdk
```
Usage: Implement the SDK in your chatbot project.

Streaming Example

This example demonstrates streaming capabilities in Edge and Serverless environments:

import { ConversationClient } from '@chatbotkit/sdk/conversation/index.js'

const client = new ConversationClient(/* configuration */)

for await (const { type, data } of client
  .complete(null, { model: 'gpt-4', messages })
  .stream()) {
  if (type === 'token') {
    process.stdout.write(data.token)
  }
}

NextGen Example for Next.js

This example showcases how to build advanced conversational AI with streaming, function calls, server-side rendering and much more in a Next.js project:

// file: ./app/page.jsx
import ChatArea from '../components/ChatArea.jsx'

export default function Page() {
  return <ChatArea />
}

// file: ./components/ChatArea.jsx
'use client'

import { useContext } from 'react'

import { complete } from '../actions/conversation.jsx'

import { ChatInput, ConversationContext } from '@chatbotkit/react'
import ConversationManager from '@chatbotkit/react/components/ConversationManager'

export function ChatMessages() {
  const {
    thinking,

    text,
    setText,

    messages,

    submit,
  } = useContext(ConversationContext)

  return (
    <div>
      <div>
        {messages.map(({ id, type, text, children }) => {
          switch (type) {
            case 'user':
              return (
                <div key={id}>
                  <div>
                    <strong>user:</strong> {text}
                  </div>
                </div>
              )

            case 'bot':
              return (
                <div key={id}>
                  <div>
                    <strong>bot:</strong> {text}
                  </div>
                  {children ? <div>{children}</div> : null}
                </div>
              )
          }
        })}
        {thinking ? (
          <div key="thinking">
            <strong>bot:</strong> thinking...
          </div>
        ) : null}
      </div>
      <ChatInput
        value={text}
        onChange={(e) => setText(e.target.value)}
        onSubmit={submit}
        placeholder="Type something..."
        style={{
          border: 0,
          outline: 'none',
          resize: 'none',
          width: '100%',
          marginTop: '10px',
        }}
      />
    </div>
  )
}

export default function ChatArea() {
  return (
    <ConversationManager endpoint={complete}>
      <ChatMessages />
    </ConversationManager>
  )
}

// file: ./actions/conversation.jsx
'use server'

import CalendarEvents from '../components/CalendarEvents.jsx'

import { streamComplete } from '@chatbotkit/react/actions/complete'
import { ChatBotKit } from '@chatbotkit/sdk'

const cbk = new ChatBotKit({
  secret: process.env.CHATBOTKIT_API_SECRET,
})

export async function complete({ messages }) {
  return streamComplete({
    client: cbk.conversation,

    messages,

    functions: [
      {
        name: 'getUserName',
        description: 'Get the authenticated user name',
        parameters: {},
        handler: async () => {
          return 'John Doe'
        },
      },

      {
        name: 'getCalendarEvents',
        description: 'Get a list of calendar events',
        parameters: {},
        handler: async () => {
          const events = [
            { id: 1, title: 'Meeting with Jane Doe' },
            { id: 2, title: 'Meeting with Jill Doe' },
          ]

          return {
            children: <CalendarEvents events={events} />,

            result: {
              events,
            },
          }
        },
      },

      {
        name: 'declineCalendarEvent',
        description: 'Decline a calendar event',
        parameters: {
          type: 'object',
          properties: {
            id: {
              type: 'number',
              description: 'The ID of the event to decline',
            },
          },
          required: ['id'],
        },
        handler: async ({ id }) => {
          return `You have declined the event with ID ${id}`
        },
      },
    ],
  })
}

Basic Next.js Example

This quick example demonstrates how to use the SDK in a Next.js project:

// file: ./pages/index.js
import { AutoTextarea, useConversationManager } from '@chatbotkit/react'

export default function Index() {
  const {
    thinking,

    text,
    setText,

    messages,

    submit,
  } = useConversationManager({
    endpoint: '/api/conversation/complete',
  })

  function handleOnKeyDown(event) {
    if (event.keyCode === 13) {
      event.preventDefault()

      submit()
    }
  }

  return (
    <div style={{ fontFamily: 'monospace', padding: '10px' }}>
      {messages.map(({ id, type, text }) => (
        <div key={id}>
          <strong>{type}:</strong> {text}
        </div>
      ))}
      {thinking && (
        <div key="thinking">
          <strong>bot:</strong> thinking...
        </div>
      )}
      <AutoTextarea
        value={text}
        onChange={(e) => setText(e.target.value)}
        onKeyDown={handleOnKeyDown}
        placeholder="Type something..."
        style={{
          border: 0,
          outline: 'none',
          resize: 'none',
          width: '100%',
          marginTop: '10px',
        }}
      />
    </div>
  )
}

// file: ./pages/api/conversation/complete.js
import { ChatBotKit } from '@chatbotkit/sdk'
import { stream } from '@chatbotkit/next/edge'

const cbk = new ChatBotKit({
  secret: process.env.CHATBOTKIT_API_SECRET,
})

export default async function handler(req) {
  const { messages } = await req.json()

  return stream(cbk.conversation.complete(null, { messages }))
}

export const config = {
  runtime: 'edge',
}

Examples

Explore a wide range of examples here.

Some notable examples include:

Platform	Example	Description
Next.js	Stateless Chat (App Router + RSC + Functions + Function Request)	A stateless chatbot example, where the conversation is managed by the client and the server. This example uses the App Router and Server Actions as well AI functions with function requests. This is a powerful example to demonstrate the full capabilities of the ChatBotKit conversational AI platform.
Next.js	Stateless Chat (App Router + RSC + Functions)	A stateless chatbot example, where the conversation is managed by the client and the server. This example uses the App Router and Server Actions as well AI functions.
Next.js	Stateless Chat (App Router + RSC)	A stateless chatbot example, where the conversation is managed by the client and the server. This example uses the App Router and Server Actions.
Next.js	Stateless Chat (App Router)	A stateless chatbot example, where the conversation is managed by the client. This example uses the App Router.
Next.js	Stateless Chat	A stateless chatbot example, where the conversation is managed by the client.
Next.js	Basic Chat	A basic chatbot example, where the conversation is managed by ChatBotKit.
Next.js	NextAuth Example	Shows how to combine NextAuth and ChatBotKit.
Node	GPT4 Streaming AI chatbot	A simple streaming AI chatbot example.
Cloudflare Workers	GPT4 AI chatbot	A streaming AI chatbot example for Cloudflare Workers.

Stability

All SDK features are considered unstable unless explicitly marked as stable. Stability is indicated by the presence of a @stable tag in the documentation.

Documentation

Type Documentation: Detailed information on available types here.
Platform Documentation: Comprehensive guide to ChatBotKit here.
Platform Tutorials: Step-by-step tutorials for ChatBotKit here.

Contributing

Encounter a bug or want to contribute? Open an issue or submit a pull request on our official GitHub repository.

Setup

The project is setup as a monorepo using pnpm and turbo build. Clone the reporitory and run the following commands to get started:

pnpm install

To perform type and lint checks, run:

pnpm check
pnpm lint

To build the project, run:

pnpm build

To build a specific package, run:

pnpm -F @chatbotkit/${PACKAGE_NAME} build

For Tasks:

Click tags to check more tools for each tasks

build chatbots manage conversations review chat history customize bot behavior integrate with messaging platforms

For Jobs:

chatbot developer ai engineer conversational ai specialist bot developer ai solutions architect

Alternative AI tools for node-sdk

Similar Open Source Tools

node-sdk

github

: 52

agentipy

Agentipy is a powerful toolkit for interacting with the Solana blockchain, providing easy-to-use functions for token operations, trading, yield farming, LangChain integration, performance tracking, token data retrieval, pump & fun token launching, Meteora DLMM pool creation, and more. It offers features like token transfers, balance checks, staking, deploying new tokens, requesting faucet funds, trading with customizable slippage, yield farming with Lulo, and accessing LangChain tools for enhanced blockchain interactions. Users can also track current transactions per second (TPS), fetch token data by ticker or address, launch pump & fun tokens, create Meteora DLMM pools, buy/sell tokens with Raydium liquidity, and burn/close token accounts individually or in batches.

github

: 257

pixeltable

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

github

: 143

actor-core

Actor-core is a lightweight and flexible library for building actor-based concurrent applications in Java. It provides a simple API for creating and managing actors, as well as handling message passing between actors. With actor-core, developers can easily implement scalable and fault-tolerant systems using the actor model.

github

: 458

kernel-memory

Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing. KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications. Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources. Designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT, Kernel Memory enhances data-driven features in applications built for most popular AI platforms.

github

: 1.8k

Crane

Crane is a high-performance inference framework leveraging Rust's Candle for maximum speed on CPU/GPU. It focuses on accelerating LLM inference speed with optimized kernels, reducing development overhead, and ensuring portability for running models on both CPU and GPU. Supported models include TTS systems like Spark-TTS and Orpheus-TTS, foundation models like Qwen2.5 series and basic LLMs, and multimodal models like Namo-R1 and Qwen2.5-VL. Key advantages of Crane include blazing-fast inference outperforming native PyTorch, Rust-powered to eliminate C++ complexity, Apple Silicon optimized for GPU acceleration via Metal, and hardware agnostic with a unified codebase for CPU/CUDA/Metal execution. Crane simplifies deployment with the ability to add new models with less than 100 lines of code in most cases.

github

: 66

simba

Simba is an open source, portable Knowledge Management System (KMS) designed to seamlessly integrate with any Retrieval-Augmented Generation (RAG) system. It features a modern UI and modular architecture, allowing developers to focus on building advanced AI solutions without the complexities of knowledge management. Simba offers a user-friendly interface to visualize and modify document chunks, supports various vector stores and embedding models, and simplifies knowledge management for developers. It is community-driven, extensible, and aims to enhance AI functionality by providing a seamless integration with RAG-based systems.

github

: 1.1k

ReasonFlux

ReasonFlux is a revolutionary template-augmented reasoning paradigm that empowers a 32B model to outperform other models in reasoning tasks. The repository provides official resources for the paper 'ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates', including the latest released model ReasonFlux-F1-32B. It includes updates, dataset links, model zoo, getting started guide, training instructions, evaluation details, inference examples, performance comparisons, reasoning examples, preliminary work references, and citation information.

github

: 351

FalkorDB

FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph. Primary features: * Adopting the Property Graph Model * Nodes (vertices) and Relationships (edges) that may have attributes * Nodes can have multiple labels * Relationships have a relationship type * Graphs represented as sparse adjacency matrices * OpenCypher with proprietary extensions as a query language * Queries are translated into linear algebra expressions

github

: 929

StableToolBench

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features such as a Virtual API System with caching and API simulators, a new set of solvable queries determined by LLMs, and a Stable Evaluation System using GPT-4. The Virtual API Server can be set up either by building from source or using a prebuilt Docker image. Users can test the server using provided scripts and evaluate models with Solvable Pass Rate and Solvable Win Rate metrics. The tool also includes model experiments results comparing different models' performance.

github

: 59

AutoAudit

AutoAudit is an open-source large language model specifically designed for the field of network security. It aims to provide powerful natural language processing capabilities for security auditing and network defense, including analyzing malicious code, detecting network attacks, and predicting security vulnerabilities. By coupling AutoAudit with ClamAV, a security scanning platform has been created for practical security audit applications. The tool is intended to assist security professionals with accurate and fast analysis and predictions to combat evolving network threats.

github

: 201

TempCompass

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

github

: 71

unsloth

Unsloth is a tool that allows users to fine-tune large language models (LLMs) 2-5x faster with 80% less memory. It is a free and open-source tool that can be used to fine-tune LLMs such as Gemma, Mistral, Llama 2-5, TinyLlama, and CodeLlama 34b. Unsloth supports 4-bit and 16-bit QLoRA / LoRA fine-tuning via bitsandbytes. It also supports DPO (Direct Preference Optimization), PPO, and Reward Modelling. Unsloth is compatible with Hugging Face's TRL, Trainer, Seq2SeqTrainer, and Pytorch code. It is also compatible with NVIDIA GPUs since 2018+ (minimum CUDA Capability 7.0).

github

: 35.2k

Scrapegraph-ai

ScrapeGraphAI is a web scraping Python library that utilizes LLM and direct graph logic to create scraping pipelines for websites and local documents. It offers various standard scraping pipelines like SmartScraperGraph, SearchGraph, SpeechGraph, and ScriptCreatorGraph. Users can extract information by specifying prompts and input sources. The library supports different LLM APIs such as OpenAI, Groq, Azure, and Gemini, as well as local models using Ollama. ScrapeGraphAI is designed for data exploration and research purposes, providing a versatile tool for extracting information from web pages and generating outputs like Python scripts, audio summaries, and search results.

github

: 18.6k

EasyEdit

EasyEdit is a Python package for edit Large Language Models (LLM) like `GPT-J`, `Llama`, `GPT-NEO`, `GPT2`, `T5`(support models from **1B** to **65B**), the objective of which is to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. It is designed to be easy to use and easy to extend.

github

: 2.2k

BrowserAI

BrowserAI is a production-ready tool that allows users to run AI models directly in the browser, offering simplicity, speed, privacy, and open-source capabilities. It provides WebGPU acceleration for fast inference, zero server costs, offline capability, and developer-friendly features. Perfect for web developers, companies seeking privacy-conscious AI solutions, researchers experimenting with browser-based AI, and hobbyists exploring AI without infrastructure overhead. The tool supports various AI tasks like text generation, speech recognition, and text-to-speech, with pre-configured popular models ready to use. It offers a simple SDK with multiple engine support and seamless switching between MLC and Transformers engines.

github

: 856

For similar tasks

agentcloud

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

github

: 583

zep-python

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

github

: 60

lollms

LoLLMs Server is a text generation server based on large language models. It provides a Flask-based API for generating text using various pre-trained language models. This server is designed to be easy to install and use, allowing developers to integrate powerful text generation capabilities into their applications.

github

: 287

LlamaIndexTS

LlamaIndex.TS is a data framework for your LLM application. Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.

github

: 2.5k

semantic-kernel

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

github

: 23.8k

botpress

Botpress is a platform for building next-generation chatbots and assistants powered by OpenAI. It provides a range of tools and integrations to help developers quickly and easily create and deploy chatbots for various use cases.

github

: 13.5k

BotSharp

BotSharp is an open-source machine learning framework for building AI bot platforms. It provides a comprehensive set of tools and components for developing and deploying intelligent virtual assistants. BotSharp is designed to be modular and extensible, allowing developers to easily integrate it with their existing systems and applications. With BotSharp, you can quickly and easily create AI-powered chatbots, virtual assistants, and other conversational AI applications.

github

: 2.6k

qdrant

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

github

: 22.8k

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 620

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k