Cursive is a universal and intuitive framework for interacting with LLMs.
It works in any JavaScript runtime and has a heavy focus on extensability and developer experience.
✦ Universal - Cursive works in any runtime, including the browser, Node.js, Deno, Bun and Cloudflare Workers.
✦ Extensible - You can easily hook into any part of a completion life cycle. Be it to log, cache or modify the results.
✦ Functions - Easily describe functions that the LLM can use along with it's definition.
✦ Universal - Cursive's goal is to bridge as manu capabilities between different models as possible. Ultimately, this means that with a single interface, you can allow your users to choose any model.
✦ Informative - Cursive comes with builtin token usage and costs calculations, as accurate as possible.
✦ Reliable - Cursive comes with automatic retry and model expanding upon exceeding context length. Which you can always configure.
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Install.
npm i cursive-gpt
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Start using.
import { useCursive } from 'cursive-gpt' const cursive = useCursive({ openAI: { apiKey: 'sk-xxxx' } }) const { answer } = await cursive.ask({ prompt: 'What is the meaning of life?', })
Chaining a conversation is easy with cursive. You can pass any of the options you're used to with OpenAI's API.
const resA = await cursive.ask({
prompt: 'Give me a good name for an gecko.',
model: 'gpt-4',
maxTokens: 16,
})
console.log(resA.answer) // Zephyr
const resB = await resA.conversation.ask({
prompt: 'How would you say it in portuguese?'
})
console.log(resB.answer) // ZéfiroStreaming is also supported, and we also keep track of the tokens for you!
const result = await cursive.ask({
prompt: 'Count to 10',
stream: true,
onToken(partial) {
console.log(partial.content)
}
})
console.log(result.usage.totalTokens) // 40You can use zod to define and describe functions, along side with their execution code.
import { createFunction, useCursive, z } from 'cursive-gpt'
const cursive = useCursive({
openAI: {
apiKey: 'sk-xxxx'
}
})
const sum = createFunction({
name: 'sum',
description: 'sums two numbers',
parameters: {
a: z.number().describe('Number A'),
b: z.number().describe('Number B'),
},
async execute({ a, b }) {
return a + b
},
})
const { answer } = await cursive.ask({
prompt: 'What is the sum of 232 and 243?',
functions: [sum],
})
console.log(answer) // The sum of 232 and 243 is 475.Th
8992
e functions' result will automatically fed into the conversation and another completion will be made. If you want to prevent this, you can add pause to your function definition.
const createCharacter = createFunction({
name: 'createCharacter',
description: 'Creates a character',
parameters: {
name: z.string().describe('The name of the character'),
age: z.number().describe('The age of the character'),
},
pause: true,
async execute({ name, age }) {
return { name, age }
},
})
const { functionResult } = await cursive.ask({
prompt: 'Create a character named John who is 23 years old.',
functions: [createCharacter],
})
console.log(functionResult) // { name: 'John', age: 23 }You can hook into any part of the completion life cycle.
cursive.on('completion:after', (result) => {
console.log(result.cost.total)
console.log(result.usage.total_tokens)
})
cursive.on('completion:error', (error) => {
console.log(error)
})
cursive.ask({
prompt: 'Can androids dream of electric sheep?',
})
// 0.0002185
// 113You can create embeddings pretty easily with cursive.
const embedding = await cursive.embed('This should be a document.')This will support different types of documents and integrations pretty soon.
Cursive comes with automatic retry with backoff and model expanding upon exceeding context length -- which means that it tries again with a model with a bigger context length when it fails by running out of it.
You can configure this behavior by passing the retry and expand options to useCursive.
const cursive = useCursive({
maxRetries: 5, // 0 disables it completely
expand: {
enable: true,
defaultsTo: 'gpt-3.5-turbo-16k',
modelMapping: {
'gpt-3.5-turbo': 'gpt-3.5-turbo-16k',
'gpt-4': 'gpt-4-32k',
}
}
})- Anthropic
- Cohere
- Azure OpenAI models
- Huggingface
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