wandb/weave

pip install weave openai-agents requests

import asyncio
import requests
import weave
from agents import Agent, Runner, function_tool

weave.init("<your-team>/<your-project-name>")


@function_tool
def wikipedia_search(query: str) -> str:
    """Search Wikipedia for a topic and return its title and intro paragraph."""
    r = requests.get(
        "https://en.wikipedia.org/w/api.php",
        params={
            "action": "query",
            "generator": "search",
            "gsrsearch": query,
            "gsrlimit": 1,
            "prop": "extracts",
            "exintro": True,
            "explaintext": True,
            "format": "json",
        },
        headers={"User-Agent": "weave-demo"},
    ).json()
    page = next(iter(r["query"]["pages"].values()))
    return f"{page['title']}: {page['extract']}"


agent = Agent(
    name="Research assistant",
    instructions=(
        "You are a research assistant. Use the wikipedia_search tool to look up "
        "topics when needed, and cite the article titles you used."
    ),
    tools=[wikipedia_search],
)


async def main():
    history = []
    for question in [
        "Who founded Anthropic?",
        "What is Claude (the AI assistant)?",
        "Summarize what we discussed in one sentence.",
    ]:
        history.append({"role": "user", "content": question})
        print(f"USER: {question}")
        result = await Runner.run(agent, input=history)
        print(f"AGENT: {result.final_output}\n")
        history = result.to_input_list()


asyncio.run(main())

import weave

weave.init("<your-team>/<your-project-name>")

with weave.start_conversation(agent_name="research-bot"):
    with weave.start_turn(user_message="Who founded Anthropic?"):
        with weave.start_llm(model="gpt-4o-mini", provider_name="openai") as llm:
            llm.output("Anthropic was founded by former OpenAI researchers.")
            llm.record(
                usage=weave.Usage(input_tokens=12, output_tokens=9),
                response_model="gpt-4o-mini",
            )

pip install weave openai

import json
import weave
from openai import OpenAI

weave.init("<your-team>/<your-project-name>")


@weave.op
def extract_fruit(sentence: str) -> dict:
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": (
                    "You will be provided with unstructured data, and your task is to parse "
                    "it into one JSON object with fruit, color and flavor as keys."
                ),
            },
            {"role": "user", "content": sentence},
        ],
        temperature=0.7,
        response_format={"type": "json_object"},
    )
    extracted = response.choices[0].message.content
    return json.loads(extracted)


extract_fruit(
    "There are many fruits that were found on the recently discovered planet Goocrux. "
    "There are neoskizzles that grow there, which are purple and taste like candy."
)