LiteLLM

pip install netra-sdk litellm

npm install netra-sdk litellm

from netra import Netra
from litellm import completion
import os

# Initialize Netra
Netra.init(
    headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
    trace_content=True
)

# Use LiteLLM as normal - automatically traced
response = completion(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

import { Netra } from "netra-sdk";
import { completion } from "litellm";

// Initialize Netra
await Netra.init({
  headers: `x-api-key=${process.env.NETRA_API_KEY}`,
  traceContent: true
});

// Use LiteLLM as normal - automatically traced
const response = await completion({
  model: "gpt-3.5-turbo",
  messages: [{ role: "user", content: "Hello!" }]
});
console.log(response.choices[0].message.content);

from netra.decorators import task
from netra import SpanWrapper
from litellm import completion

@task()
def call_multiple_providers(prompt: str) -> dict:
    span = SpanWrapper("multi-provider", {
        "prompt": prompt
    }).start()
    
    results = {}
    
    # OpenAI
    openai_response = completion(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    results["openai"] = openai_response.choices[0].message.content
    
    # Anthropic
    anthropic_response = completion(
        model="claude-3-sonnet-20240229",
        messages=[{"role": "user", "content": prompt}]
    )
    results["anthropic"] = anthropic_response.choices[0].message.content
    
    span.set_attribute("providers.count", len(results))
    span.end()
    
    return results

import { task, SpanWrapper } from "netra-sdk";
import { completion } from "litellm";

@task()
async function callMultipleProviders(prompt: string): Promise<Record<string, string>> {
  const span = new SpanWrapper("multi-provider", {
    "prompt": prompt
  }).start();
  
  const results: Record<string, string> = {};
  
  // OpenAI
  const openaiResponse = await completion({
    model: "gpt-3.5-turbo",
    messages: [{ role: "user", content: prompt }]
  });
  results["openai"] = openaiResponse.choices[0].message.content;
  
  // Anthropic
  const anthropicResponse = await completion({
    model: "claude-3-sonnet-20240229",
    messages: [{ role: "user", content: prompt }]
  });
  results["anthropic"] = anthropicResponse.choices[0].message.content;
  
  span.setAttribute("providers.count", Object.keys(results).length);
  span.end();
  
  return results;
}

from netra.decorators import workflow
from netra import SpanWrapper
from litellm import completion

@workflow()
def efficient_completion(messages: list[dict], model: str = "gpt-3.5-turbo"):
    span = SpanWrapper("litellm-completion", {
        "model": model,
        "messages.count": len(messages)
    }).start()
    
    response = completion(
        model=model,
        messages=messages,
        timeout=30
    )
    
    content = response.choices[0].message.content
    span.set_attribute("response", content)
    span.set_attribute("tokens.total", response.usage.total_tokens)
    span.end()
    
    return content

import { workflow, SpanWrapper } from "netra-sdk";
import { completion } from "litellm";

@workflow()
async function efficientCompletion(messages: Array<any>, model: string = "gpt-3.5-turbo") {
  const span = new SpanWrapper("litellm-completion", {
    "model": model,
    "messages.count": messages.length
  }).start();
  
  const response = await completion({
    model: model,
    messages: messages,
    timeout: 30
  });
  
  const content = response.choices[0].message.content;
  span.setAttribute("response", content);
  span.setAttribute("tokens.total", response.usage?.total_tokens || 0);
  span.end();
  
  return content;
}

from netra.decorators import agent
from netra import SpanWrapper
from litellm import completion

@agent()
def completion_with_fallback(prompt: str, models: list[str]):
    span = SpanWrapper("fallback-completion", {
        "prompt": prompt,
        "models": ",".join(models)
    }).start()
    
    for model in models:
        try:
            response = completion(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                timeout=10
            )
            
            span.set_attribute("successful_model", model)
            span.end()
            
            return response.choices[0].message.content
        except Exception as e:
            span.set_attribute(f"error.{model}", str(e))
            continue
    
    span.set_status(code=1, message="All models failed")
    span.end()
    raise Exception("All models failed")

import { agent, SpanWrapper } from "netra-sdk";
import { completion } from "litellm";

@agent()
async function completionWithFallback(prompt: string, models: string[]) {
  const span = new SpanWrapper("fallback-completion", {
    "prompt": prompt,
    "models": models.join(",")
  }).start();
  
  for (const model of models) {
    try {
      const response = await completion({
        model: model,
        messages: [{ role: "user", content: prompt }],
        timeout: 10
      });
      
      span.setAttribute("successful_model", model);
      span.end();
      
      return response.choices[0].message.content;
    } catch (error) {
      span.setAttribute(`error.${model}`, String(error));
      continue;
    }
  }
  
  span.setStatus({ code: 1, message: "All models failed" });
  span.end();
  throw new Error("All models failed");
}

from netra.decorators import task
from netra import SpanWrapper
from litellm import completion

@task()
def stream_any_provider(model: str, prompt: str):
    stream_span = SpanWrapper("litellm-stream", {
        "model": model
    }).start()
    
    response = completion(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )
    
    full_response = ""
    for chunk in response:
        if chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            print(content, end='', flush=True)
            full_response += content
    
    stream_span.set_attribute("response", full_response)
    stream_span.end()
    
    return full_response

import { task, SpanWrapper } from "netra-sdk";
import { completion } from "litellm";

@task()
async function streamAnyProvider(model: string, prompt: string) {
  const streamSpan = new SpanWrapper("litellm-stream", {
    "model": model
  }).start();
  
  const response = await completion({
    model: model,
    messages: [{ role: "user", content: prompt }],
    stream: true
  });
  
  let fullResponse = "";
  for await (const chunk of response) {
    if (chunk.choices[0]?.delta?.content) {
      const content = chunk.choices[0].delta.content;
      process.stdout.write(content);
      fullResponse += content;
    }
  }
  
  streamSpan.setAttribute("response", fullResponse);
  streamSpan.end();
  
  return fullResponse;
}

from netra.decorators import task
from netra import SpanWrapper
from litellm import acompletion
import asyncio

@task()
async def async_completion(prompt: str):
    span = SpanWrapper("async-completion").start()
    
    response = await acompletion(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    
    content = response.choices[0].message.content
    span.set_attribute("response", content)
    span.end()
    
    return content

import { task, SpanWrapper } from "netra-sdk";
import { completion } from "litellm";

@task()
async function asyncCompletion(prompt: string) {
  const span = new SpanWrapper("async-completion").start();
  
  const response = await completion({
    model: "gpt-3.5-turbo",
    messages: [{ role: "user", content: prompt }]
  });
  
  const content = response.choices[0].message.content;
  span.setAttribute("response", content);
  span.end();
  
  return content;
}

from netra import Netra
from netra.instrumentation.instruments import InstrumentSet

Netra.init(
    headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
    trace_content=True,
    instruments={InstrumentSet.LITELLM}
)

import { Netra, NetraInstruments } from "netra-sdk";

await Netra.init({
  headers: `x-api-key=${process.env.NETRA_API_KEY}`,
  traceContent: true,
  instruments: new Set([NetraInstruments.LITELLM])
});