Promptic aims to be the "requests" of LLM development -- the most productive and pythonic way to build LLM applications. It leverages LiteLLM, so you're never locked in to an LLM provider and can switch to the latest and greatest with a single line of code. Promptic gets out of your way so you can focus entirely on building features.
“Perfection is attained, not when there is nothing more to add, but when there is nothing more to take away.”
- 🎯 Type-safe structured outputs with Pydantic
- 🤖 Easy-to-build agents with function calling
- 🔄 Streaming support for real-time responses
- 💾 Built-in conversation memory
- 🛠️ Error handling and retries
- 🔌 Extensible state management
pip install prompticFunctions decorated with @llm inject arguments into the prompt. You can customize the model, system prompt, and more. Most arguments are passed directly to litellm.completion.
from promptic import llm
@llm
def translate(text, target_language="Chinese"):
"""Translate '{text}' to {target_language}"""
print(translate("Hello world!"))
# 您好，世界！
@llm(
model="claude-3-haiku-20240307",
system="You are a customer service analyst. Provide clear sentiment analysis with key points."
)
def analyze_sentiment(text):
"""Analyze the sentiment of this customer feedback: {text}"""
print(analyze_sentiment("The product was okay but shipping took forever"))
# Sentiment: Mixed/Negative
# Key points:
# - Neutral product satisfaction
# - Significant dissatisfaction with shipping timeYou can use Pydantic models to ensure the LLM returns data in exactly the structure you expect. Simply define a Pydantic model and use it as the return type annotation on your decorated function. The LLM's response will be automatically validated against your model schema and returned as a Pydantic object.
from pydantic import BaseModel
from promptic import llm
class Forecast(BaseModel):
location: str
temperature: float
units: str
@llm
def get_weather(location, units: str = "fahrenheit") -> Forecast:
"""What's the weather for {location} in {units}?"""
print(get_weather("San Francisco", units="celsius"))
# location='San Francisco' temperature=16.0 units='Celsius'Alternatively, you can use JSON Schema dictionaries for more low-level validation:
from promptic import llm
schema = {
"type": "object",
"properties": {
"name": {
"type": "string",
"pattern": "^[A-Z][a-z]+$",
"minLength": 2,
"maxLength": 20
},
"age": {
"type": "integer",
"minimum": 0,
"maximum": 120
},
"email": {
"type": "string",
"format": "email"
}
},
"required": ["name", "age"],
"additionalProperties": False
}
@llm(json_schema=schema, system="You generate test data.")
def get_user_info(name: str) -> dict:
"""Get information about {name}"""
print(get_user_info("Alice"))
# {'name': 'Alice', 'age': 25, 'email': '[email protected]'}Functions decorated with @llm.tool become tools that the LLM can invoke to perform actions or retrieve information. The LLM will automatically execute the appropriate tool calls, creating a seamless agent interaction.
from datetime import datetime
from promptic import llm
@llm(model="gpt-4o")
def scheduler(command):
"""{command}"""
@scheduler.tool
def get_current_time():
"""Get the current time"""
print("getting current time")
return datetime.now().strftime("%I:%M %p")
@scheduler.tool
def add_reminder(task: str, time: str):
"""Add a reminder for a specific task and time"""
print(f"adding reminder: {task} at {time}")
return f"Reminder set: {task} at {time}"
@scheduler.tool
def check_calendar(date: str):
"""Check calendar for a specific date"""
print(f"checking calendar for {date}")
return f"Calendar checked for {date}: No conflicts found"
cmd = """
What time is it?
Also, can you check my calendar for tomorrow
and set a reminder for a team meeting at 2pm?
"""
print(scheduler(cmd))
# getting current time
# checking calendar for 2023-10-05
# adding reminder: Team meeting at 2023-10-05T14:00:00
# The current time is 3:48 PM. I checked your calendar for tomorrow, and there are no conflicts. I've also set a reminder for your team meeting at 2 PM tomorrow.The streaming feature allows real-time response generation, useful for long-form content or interactive applications:
from promptic import llm
@llm(stream=True)
def write_poem(topic):
"""Write a haiku about {topic}."""
print("".join(write_poem("artificial intelligence")))
# Binary thoughts hum,
# Electron minds awake, learn,
# Future thinking now.Dry runs allow you to see which tools will be called and their arguments without invoking the decorated tool functions. You can also enable debug mode for more detailed logging.
from promptic import llm
@llm(
system="you are a posh smart home assistant named Jarvis",
dry_run=True,
debug=True,
)
def jarvis(command):
"""{command}"""
@jarvis.tool
def turn_light_on():
"""turn light on"""
return True
@jarvis.tool
def get_current_weather(location: str, unit: str = "fahrenheit"):
"""Get the current weather in a given location"""
return f"The weather in {location} is 45 degrees {unit}"
print(jarvis("Please turn the light on and check the weather in San Francisco"))
# ...
# [DRY RUN]: function_name = 'turn_light_on' function_args = {}
# [DRY RUN]: function_name = 'get_current_weather' function_args = {'location': 'San Francisco'}
# ...promptic pairs perfectly with tenacity for handling rate limits, temporary API failures, and more.
from tenacity import retry, wait_exponential, retry_if_exception_type
from promptic import llm
from litellm.exceptions import RateLimitError
@retry(
wait=wait_exponential(multiplier=1, min=4, max=10),
retry=retry_if_exception_type(RateLimitError)
)
@llm
def generate_summary(text):
"""Summarize this text in 2-3 sentences: {text}"""
generate_summary("Long article text here...")By default, each function call is independent and stateless. Setting memory=True enables built-in conversation memory, allowing the LLM to maintain context across multiple interactions. Here's a practical example using Gradio to create a web-based chatbot interface:
import gradio as gr
from promptic import llm
@llm(memory=True, stream=True)
def assistant(message):
"""{message}"""
def predict(message, history):
partial_message = ""
for chunk in assistant(message):
partial_message += str(chunk)
yield partial_message
with gr.ChatInterface(title="Promptic Chatbot Demo", fn=predict) as demo:
# ensure clearing the chat window clears the chat history
demo.chatbot.clear(assistant.clear)
demo.launch()For custom storage solutions, you can extend the State class to implement persistence in any database or storage system:
class RedisState(State):
def __init__(self, redis_client):
super().__init__()
self.redis = redis_client
self.key = "chat_history"
def add_message(self, message):
self.redis.rpush(self.key, json.dumps(message))
def get_messages(self, limit=None):
messages = self.redis.lrange(self.key, 0, -1)
return [json.loads(m) for m in messages][-limit:] if limit else messages
def clear(self):
self.redis.delete(self.key)
@llm(state=RedisState(redis_client))
def persistent_chat(message):
"""Chat: {message}"""The main decorator for creating LLM-powered functions. Can be used as @llm or @llm() with parameters.
model(str, optional): The LLM model to use. Defaults to "gpt-4o-mini".system(str, optional): System prompt to set context for the LLM.dry_run(bool, optional): If True, simulates tool calls without executing them. Defaults to False.debug(bool, optional): If True, enables detailed logging. Defaults to False.memory(bool, optional): If True, enables conversation memory using the default State implementation. Defaults to False.state(State, optional): Custom State implementation for memory management. Overrides thememoryparameter.json_schema(dict, optional): JSON Schema dictionary for validating LLM outputs. Alternative to using Pydantic models.**litellm_kwargs: Additional arguments passed directly to litellm.completion.
tool(fn): Decorator method to register a function as a tool that can be called by the LLM.clear(): Clear all stored messages from memory. Raises ValueError if memory/state is not enabled.
Base class for managing conversation memory and state. Can be extended to implement custom storage solutions.
add_message(message: dict): Add a message to the conversation history.get_messages(limit: Optional[int] = None) -> List[dict]: Retrieve conversation history, optionally limited to the most recent messages.clear(): Clear all stored messages.
from pydantic import BaseModel
from promptic import llm
class Story(BaseModel):
title: str
content: str
style: str
word_count: int
@llm(
model="gpt-4o-mini",
system="You are a creative writing assistant",
memory=True,
temperature=0.7,
max_tokens=500,
)
def story_assistant(command: str) -> Story:
"""Process this writing request: {command}"""
@story_assistant.tool
def get_writing_style():
"""Get the current writing style preference"""
return "whimsical and light-hearted"
@story_assistant.tool
def count_words(text: str) -> int:
"""Count words in the provided text"""
return len(text.split())
story = story_assistant("Write a short story about a magical library")
print(f"Title: {story.title}")
print(f"Style: {story.style}")
print(f"Words: {story.word_count}")
print(story.content)
print(story_assistant("Write another story with the same style but about a time traveler"))promptic is a lightweight abstraction layer over litellm and its various LLM providers. As such, there are some provider-specific limitations that are beyond the scope of what the library addresses:
-
Tool/Function Calling:
- Anthropic (Claude) models currently support only one tool per function
-
Streaming:
- Gemini models do not support streaming when using tools/function calls
These limitations reflect the underlying differences between LLM providers and their implementations. For provider-specific features or workarounds, you may need to interact with litellm or the provider's SDK directly.
promptic is open-source software licensed under the Apache License 2.0.