Build intelligent agents quickly — inspired by the power of "Agentic workflows" and the Stanford DSPy paper. Seamlessly integrates with multiple LLMs and VectorDBs to build RAG pipelines or collaborative agents that can solve complex problems. Advanced features streaming validation, multi-modal DSPy, etc.
We've renamed from "llmclient" to "ax" to highlight our focus on powering agentic workflows. We agree with many experts like "Andrew Ng" that agentic workflows are the key to unlocking the true power of large language models and what can be achieved with in-context learning. Also, we are big fans of the Stanford DSPy paper, and this library is the result of all of this coming together to build a powerful framework for you to build with.
- Support for various LLMs and Vector DBs
- Prompts auto-generated from simple signatures
- Build Agents that can call other agents
- Convert docs of any format to text
- RAG, smart chunking, embedding, querying
- Works with Vercel AI SDK
- Output validation while streaming
- Multi-modal DSPy supported
- Automatic prompt tuning using optimizers
- OpenTelemetry tracing / observability
- Production ready Typescript code
- Lite weight, zero-dependencies
Efficient type-safe prompts are auto-generated from a simple signature. A prompt signature is made up of a "task description" inputField:type "field description" -> "outputField:type. The idea behind prompt signatures is based on work done in the "Demonstrate-Search-Predict" paper.
You can have multiple input and output fields, and each field can be of the types string, number, boolean, JSON, or an array of any of these, e.g., string[]. When a type is not defined, it defaults to string. The underlying AI is encouraged to generate the correct JSON when the JSON type is used.
| Provider | Best Models | Tested |
|---|---|---|
| OpenAI | GPT: All 4 models | 🟢 100% |
| Azure OpenAI | GPT: All 4 models | 🟢 100% |
| Together | Several OSS Models | 🟢 100% |
| Cohere | CommandR, Command | 🟢 100% |
| Anthropic | Claude 2, Claude 3 | 🟢 100% |
| Mistral | 7B, 8x7B, S, L | 🟢 100% |
| Groq | Lama2-70B, Mixtral-8x7b | 🟢 100% |
| DeepSeek | Chat and Code | 🟢 100% |
| Ollama | All models | 🟢 100% |
| Google Gemini | Gemini: Flash, Pro | 🟢 100% |
| Hugging Face | OSS Model | 🟡 50% |
| Reka | Core, Flash, Edge | 🟡 50% |
npm install @ax-llm/ax
# or
yarn add @ax-llm/aximport { AxAI, AxChainOfThought } from '@ax-llm/ax';
const textToSummarize = `
The technological singularity—or simply the singularity[1]—is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.[2][3] ...`;
const ai = new AxAI({
name: 'openai',
apiKey: process.env.OPENAI_APIKEY as string
});
const gen = new AxChainOfThought(
ai,
`textToSummarize -> shortSummary "summarize in 5 to 10 words"`
);
const res = await gen.forward({ textToSummarize });
console.log('>', res);Use the agent prompt (framework) to build agents that work with other agents to complete tasks. Agents are easy to make with prompt signatures. Try out the agent example.
# npm run tsx ./src/examples/agent.ts
const researcher = new AxAgent(ai, {
name: 'researcher',
description: 'Researcher agent',
signature: `physicsQuestion "physics questions" -> answer "reply in bullet points"`
});
const summarizer = new AxAgent(ai, {
name: 'summarizer',
description: 'Summarizer agent',
signature: `text "text so summarize" -> shortSummary "summarize in 5 to 10 words"`
});
const agent = new AxAgent(ai, {
name: 'agent',
description: 'A an agent to research complex topics',
signature: `question -> answer`,
agents: [researcher, summarizer]
});
agent.forward({ questions: "How many atoms are there in the universe" })Vector databases are critical to building LLM workflows. We have clean abstractions over popular vector databases and our own quick in-memory vector database.
| Provider | Tested |
|---|---|
| In Memory | 🟢 100% |
| Weaviate | 🟢 100% |
| Cloudflare | 🟡 50% |
| Pinecone | 🟡 50% |
// Create embeddings from text using an LLM
const ret = await this.ai.embed({ texts: 'hello world' });
// Create an in memory vector db
const db = new axDB('memory');
// Insert into vector db
await this.db.upsert({
id: 'abc',
table: 'products',
values: ret.embeddings[0]
});
// Query for similar entries using embeddings
const matches = await this.db.query({
table: 'products',
values: embeddings[0]
});Alternatively you can use the AxDBManager which handles smart chunking, embedding and querying everything
for you, it makes things almost too easy.
const manager = new AxDBManager({ ai, db });
await manager.insert(text);
const matches = await manager.query(
'John von Neumann on human intelligence and singularity.'
);
console.log(matches);Using documents like PDF, DOCX, PPT, XLS, etc., with LLMs is a huge pain. We make it easy with Apache Tika, an open-source document processing engine.
Launch Apache Tika
docker run -p 9998:9998 apache/tikaConvert documents to text and embed them for retrieval using the AxDBManager, which also supports a reranker and query rewriter. Two default implementations, AxDefaultResultReranker and AxDefaultQueryRewriter, are available.
const tika = new AxApacheTika();
const text = await tika.convert('/path/to/document.pdf');
const manager = new AxDBManager({ ai, db });
await manager.insert(text);
const matches = await manager.query('Find some text');
console.log(matches);When using models like GPT-4o and Gemini that support multi-modal prompts, we support using image fields, and this works with the whole DSP pipeline.
const image = fs
.readFileSync('./src/examples/assets/kitten.jpeg')
.toString('base64');
const gen = new AxChainOfThought(ai, `question, animalImage:image -> answer`);
const res = await gen.forward({
question: 'What family does this animal belong to?',
animalImage: { mimeType: 'image/jpeg', data: image }
});We support parsing output fields and function execution while streaming. This allows for fail-fast and error correction without waiting for the whole output, saving tokens and costs and reducing latency. Assertions are a powerful way to ensure the output matches your requirements; they also work with streaming.
// setup the prompt program
const gen = new AxChainOfThought(
ai,
`startNumber:number -> next10Numbers:number[]`
);
// add a assertion to ensure that the number 5 is not in an output field
gen.addAssert(({ next10Numbers }: Readonly<{ next10Numbers: number[] }>) => {
return next10Numbers ? !next10Numbers.includes(5) : undefined;
}, 'Numbers 5 is not allowed');
// run the program with streaming enabled
const res = await gen.forward({ startNumber: 1 }, { stream: true });The above example allows you to validate entire output fields as they are streamed in. This validation works with streaming and when not streaming and is triggered when the whole field value is available. For true validation while streaming, check out the example below. This will massively improve performance and save tokens at scale in production.
// add a assertion to ensure all lines start with a number and a dot.
gen.addStreamingAssert(
'answerInPoints',
(value: string) => {
const re = /^\d+\./;
// split the value by lines, trim each line,
// filter out empty lines and check if all lines match the regex
return value
.split('\n')
.map((x) => x.trim())
.filter((x) => x.length > 0)
.every((x) => re.test(x));
},
'Lines must start with a number and a dot. Eg: 1. This is a line.'
);
// run the program with streaming enabled
const res = await gen.forward(
{
question: 'Provide a list of optimizations to speedup LLM inference.'
},
{ stream: true, debug: true }
);A special router that uses no LLM calls, only embeddings, to route user requests smartly.
Use the Router to efficiently route user queries to specific routes designed to handle certain questions or tasks. Each route is tailored to a particular domain or service area. Instead of using a slow or expensive LLM to decide how user input should be handled, use our fast "Semantic Router," which uses inexpensive and fast embedding queries.
# npm run tsx ./src/examples/routing.ts
const customerSupport = new AxRoute('customerSupport', [
'how can I return a product?',
'where is my order?',
'can you help me with a refund?',
'I need to update my shipping address',
'my product arrived damaged, what should I do?'
]);
const technicalSupport = new AxRoute('technicalSupport', [
'how do I install your software?',
'I’m having trouble logging in',
'can you help me configure my settings?',
'my application keeps crashing',
'how do I update to the latest version?'
]);
const ai = new AxAI({ name: 'openai', apiKey: process.env.OPENAI_APIKEY as string });
const router = new AxRouter(ai);
await router.setRoutes(
[customerSupport, technicalSupport],
{ filename: 'router.json' }
);
const tag = await router.forward('I need help with my order');
if (tag === "customerSupport") {
...
}
if (tag === "technicalSupport") {
...
}Install the ax provider package
npm i @ax-llm/ax-ai-sdk-providerThen use it with the AI SDK, you can either use the AI provider or the Agent Provider
const ai = new AxAI({
name: 'openai',
apiKey: process.env['OPENAI_APIKEY'] ?? "",
});
// Create a model using the provider
const model = new AxAIProvider(ai);
export const foodAgent = new AxAgent(ai, {
name: 'food-search',
description:
'Use this agent to find restaurants based on what the customer wants',
signature,
functions
})
// Get vercel ai sdk state
const aiState = getMutableAIState()
// Create an agent for a specific task
const foodAgent = new AxAgentProvider({
agent: foodAgent,
updateState: (state) => {
aiState.done({ ...aiState.get(), state })
},
generate: async ({ restaurant, priceRange }) => {
return (
<BotCard>
<h1>{restaurant as string} {priceRange as string}</h1>
</BotCard>
)
}
})
// Use with streamUI a critical part of building chat UIs in the AI SDK
const result = await streamUI({
model,
initial: <SpinnerMessage />,
messages: [
// ...
],
text: ({ content, done, delta }) => {
// ...
},
tools: {
// @ts-ignore
'find-food': foodAgent,
}
})The ability to trace and observe your llm workflow is critical to building production workflows. OpenTelemetry is an industry-standard, and we support the new gen_ai attribute namespace.
import { trace } from '@opentelemetry/api';
import {
BasicTracerProvider,
ConsoleSpanExporter,
SimpleSpanProcessor
} from '@opentelemetry/sdk-trace-base';
const provider = new BasicTracerProvider();
provider.addSpanProcessor(new SimpleSpanProcessor(new ConsoleSpanExporter()));
trace.setGlobalTracerProvider(provider);
const tracer = trace.getTracer('test');
const ai = new AxAI({
name: 'ollama',
config: { model: 'nous-hermes2' },
options: { tracer }
});
const gen = new AxChainOfThought(
ai,
`text -> shortSummary "summarize in 5 to 10 words"`
);
const res = await gen.forward({ text });{
"traceId": "ddc7405e9848c8c884e53b823e120845",
"name": "Chat Request",
"id": "d376daad21da7a3c",
"kind": "SERVER",
"timestamp": 1716622997025000,
"duration": 14190456.542,
"attributes": {
"gen_ai.system": "Ollama",
"gen_ai.request.model": "nous-hermes2",
"gen_ai.request.max_tokens": 500,
"gen_ai.request.temperature": 0.1,
"gen_ai.request.top_p": 0.9,
"gen_ai.request.frequency_penalty": 0.5,
"gen_ai.request.llm_is_streaming": false,
"http.request.method": "POST",
"url.full": "http://localhost:11434/v1/chat/completions",
"gen_ai.usage.completion_tokens": 160,
"gen_ai.usage.prompt_tokens": 290
}
}You can tune your prompts using a larger model to help them run more efficiently and give you better results. This is done by using an optimizer like AxBootstrapFewShot with and examples from the popular HotPotQA dataset. The optimizer generates demonstrations demos which when used with the prompt help improve its efficiency.
// Download the HotPotQA dataset from huggingface
const hf = new AxHFDataLoader({
dataset: 'hotpot_qa',
split: 'train'
});
const examples = await hf.getData<{ question: string; answer: string }>({
count: 100,
fields: ['question', 'answer']
});
const ai = new AxAI({
name: 'openai',
apiKey: process.env.OPENAI_APIKEY as string
});
// Setup the program to tune
const program = new AxChainOfThought<{ question: string }, { answer: string }>(
ai,
`question -> answer "in short 2 or 3 words"`
);
// Setup a Bootstrap Few Shot optimizer to tune the above program
const optimize = new AxBootstrapFewShot<
{ question: string },
{ answer: string }
>({
program,
examples
});
// Setup a evaluation metric em, f1 scores are a popular way measure retrieval performance.
const metricFn: AxMetricFn = ({ prediction, example }) =>
emScore(prediction.answer as string, example.answer as string);
// Run the optimizer and remember to save the result to use later
const result = await optimize.compile(metricFn);
And to use the generated demos with the above ChainOfThought program
const ai = new AxAI({
name: 'openai',
apiKey: process.env.OPENAI_APIKEY as string
});
// Setup the program to use the tuned data
const program = new AxChainOfThought<{ question: string }, { answer: string }>(
ai,
`question -> answer "in short 2 or 3 words"`
);
// load tuning data
program.loadDemos('demos.json');
const res = await program.forward({
question: 'What castle did David Gregory inherit?'
});
console.log(res);| Function | Name | Description |
|---|---|---|
| JS Interpreter | AxJSInterpreter | Execute JS code in a sandboxed env |
| Docker Sandbox | AxDockerSession | Execute commands within a docker environment |
| Embeddings Adapter | AxEmbeddingAdapter | Fetch and pass embedding to your function |
Use the tsx command to run the examples. It makes the node run typescript code. It also supports using an .env file to pass the AI API Keys instead of putting them in the command line.
OPENAI_APIKEY=openai_key npm run tsx ./src/examples/marketing.ts| Example | Description |
|---|---|
| customer-support.ts | Extract valuable details from customer communications |
| food-search.ts | Use multiple APIs are used to find dinning options |
| marketing.ts | Generate short effective marketing sms messages |
| vectordb.ts | Chunk, embed and search text |
| fibonacci.ts | Use the JS code interpreter to compute fibonacci |
| summarize.ts | Generate a short summary of a large block of text |
| chain-of-thought.ts | Use chain-of-thought prompting to answer questions |
| rag.ts | Use multi-hop retrieval to answer questions |
| rag-docs.ts | Convert PDF to text and embed for rag search |
| react.ts | Use function calling and reasoning to answer questions |
| agent.ts | Agent framework, agents can use other agents, tools etc |
| qna-tune.ts | Use an optimizer to improve prompt efficiency |
| qna-use-tuned.ts | Use the optimized tuned prompts |
| streaming1.ts | Output fields validation while streaming |
| streaming2.ts | Per output field validation while streaming |
| smart-hone.ts | Agent looks for dog in smart home |
| multi-modal.ts | Use an image input along with other text inputs |
| balancer.ts | Balance between various llm's based on cost, etc |
| docker.ts | Use the docker sandbox to find files by description |
Large language models (LLMs) are becoming really powerful and have reached a point where they can work as the backend for your entire product. However, there's still a lot of complexity to manage from using the correct prompts, models, streaming, function calls, error correction, and much more. We aim to package all this complexity into a well-maintained, easy-to-use library that can work with all state-of-the-art LLMs. Additionally, we are using the latest research to add new capabilities like DSPy to the library.
// Pick a LLM
const ai = new AxOpenAI({ apiKey: process.env.OPENAI_APIKEY } as AxOpenAIArgs);// Signature defines the inputs and outputs of your prompt program
const cot = new ChainOfThought(ai, `question:string -> answer:string`, { mem });// Pass in the input fields defined in the above signature
const res = await cot.forward({ question: 'Are we in a simulation?' });const res = await ai.chat([
{ role: "system", content: "Help the customer with his questions" }
{ role: "user", content: "I'm looking for a Macbook Pro M2 With 96GB RAM?" }
]);// define one or more functions and a function handler
const functions = [
{
name: 'getCurrentWeather',
description: 'get the current weather for a location',
parameters: {
type: 'object',
properties: {
location: {
type: 'string',
description: 'location to get weather for'
},
units: {
type: 'string',
enum: ['imperial', 'metric'],
default: 'imperial',
description: 'units to use'
}
},
required: ['location']
},
func: async (args: Readonly<{ location: string; units: string }>) => {
return `The weather in ${args.location} is 72 degrees`;
}
}
];const cot = new AxReAct(ai, `question:string -> answer:string`, { functions });const ai = new AxOpenAI({ apiKey: process.env.OPENAI_APIKEY } as AxOpenAIArgs);
ai.setOptions({ debug: true });We're happy to help reach out if you have questions or join the Discord twitter/dosco
Improve the function naming and description. Be very clear about what the function does. Also, ensure the function parameters have good descriptions. The descriptions can be a little short but need to be precise.
You can pass a configuration object as the second parameter when creating a new LLM object.
const apiKey = process.env.OPENAI_APIKEY;
const conf = AxOpenAIBestConfig();
const ai = new AxOpenAI({ apiKey, conf } as AxOpenAIArgs);const conf = axOpenAIDefaultConfig(); // or OpenAIBestOptions()
conf.maxTokens = 2000;const conf = axOpenAIDefaultConfig(); // or OpenAIBestOptions()
conf.model = OpenAIModel.GPT4Turbo;It is essential to remember that we should only run npm install from the root directory. This prevents the creation of nested package-lock.json files and avoids non-deduplicated node_modules.
Adding new dependencies in packages should be done with e.g. npm install lodash --workspace=ax (or just modify the appropriate package.json and run npm install from root).