Agent Dingo

A microframework for building LLM-powered pipelines and agents.

Dingo is a compact LLM orchestration framework designed for straightforward development of production-ready LLM-powered applications. It combines simplicity with flexibility, allowing for the efficient construction of pipelines and agents, while maintaining a high level of control over the process.

Support us 🤝

You can support the project in the following ways:

• ⭐ Star Dingo on GitHub (click the star button in the top right corner)
• 💡 Provide your feedback or propose ideas in the issues section or Discord
• 📰 Post about Dingo on LinkedIn or other platforms
• 🔗 Check out our other projects: Scikit-LLM, Falcon

Quick Start & Documentation 🚀

Step 1: Install agent-dingo

pip install agent-dingo

Step 2: Configure your OpenAI API key

export OPENAI_API_KEY=<YOUR_KEY>

Step 3: Build your pipeline

Example 1 (Linear Pipeline):

from agent_dingo.llm.openai import OpenAI
from agent_dingo.core.blocks import PromptBuilder
from agent_dingo.core.message import UserMessage
from agent_dingo.core.state import ChatPrompt


# Model
gpt = OpenAI("gpt-3.5-turbo")

# Summary prompt block
summary_pb = PromptBuilder(
    [UserMessage("Summarize the text in 10 words: ```{text}```.")]
)

# Translation prompt block
translation_pb = PromptBuilder(
    [UserMessage("Translate the text into {language}: ```{summarized_text}```.")],
    from_state=["summarized_text"],
)

# Pipeline
pipeline = summary_pb >> gpt >> translation_pb >> gpt

input_text = """
Dingo is an ancient lineage of dog found in Australia, exhibiting a lean and sturdy physique adapted for speed and endurance, dingoes feature a wedge-shaped skull and come in colorations like light ginger, black and tan, or creamy white. They share a close genetic relationship with the New Guinea singing dog, diverging early from the domestic dog lineage. Dingoes typically form packs composed of a mated pair and their offspring, indicating social structures that have persisted through their history, dating back approximately 3,500 years in Australia.
"""

output = pipeline.run(text = input_text, language = "french")
print(output)

Example 2 (Agent):

from agent_dingo.agent import Agent
from agent_dingo.llm.openai import OpenAI
import requests

llm = OpenAI(model="gpt-3.5-turbo")
agent = Agent(llm, max_function_calls=3)

@agent.function
def get_temperature(city: str) -> str:
    """Retrieves the current temperature in a city.

    Parameters
    ----------
    city : str
        The city to get the temperature for.

    Returns
    -------
    str
        String representation of the json response from the weather api.
    """
    base_url = "https://api.openweathermap.org/data/2.5/weather"
    params = {
        "q": city,
        "appid": "<openweathermap_api_key>",
        "units": "metric"
    }
    response = requests.get(base_url, params=params)
    data = response.json()
    return str(data)

pipeline = agent.as_pipeline()

For a more detailed overview and additional examples, please refer to the documentation.