python-easy-llm 0.1.2

A lightweight LLM wrapper that unifies prompt, RAG and multi-provider calls

EasyLLM

中文文档 🇨🇳

EasyLLM is a lightweight LLM wrapper library that helps you interact with large language models in the most intuitive and natural way.
Whether it's prompt engineering, structured output, or multi-vendor model access, EasyLLM is designed to make development simpler and more enjoyable.

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🚀 Features

• 🔁 Unified Interface: Supports multiple model providers (OpenAI, DeepSeek, Moonshot, Kimi, Claude, etc.) with consistent API usage.
• 🧠 Prompt-as-Content: Write prompts in Markdown + Jinja2 instead of verbose messages dictionaries.
• 🧩 Structured Output Support: Enable JSON-mode to receive structured responses from models — great for product integration.
• 🛠️ Easy Integration: Minimal dependencies, easily embedded into any Python project with no learning curve.

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📦 Installation

pip install python-easy-llm==0.1.2

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✨ Quick Example: Calling an LLM

from easyllm import LLM
import os

llm = LLM(
    model_name="deepseek-chat",
    model_provider="deepseek",
    api_key=os.environ["DEEPSEEK_API_KEY"]
)

response = llm("What is 1 + 1?")
print(response)

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📄 Example: Structured Output + Jinja2 Prompt Template

from jinja2 import Template
from easyllm import LLM
import os

llm = LLM(
    model_name="deepseek-chat",
    model_provider="deepseek",
    api_key=os.environ["DEEPSEEK_API_KEY"]
)

prompt_t = Template("""
# System
You are a {{ domain }} assistant. Based on the user's interests and skills, recommend a suitable career path. Please respond in JSON format:
{
  "recommended_job": string,
  "reason": string
}

# User
I'm interested in tech, enjoy programming, and I'm introverted with average communication skills. What job is a good fit for me?
""")

prompt = prompt_t.render(domain="career recommendation")
response = llm(prompt, json_mode=True)
print(response)

Sample output:

{
  "recommended_job": "Backend Developer",
  "reason": "You enjoy programming, have strong technical interests, and prefer limited interpersonal interaction. Backend development is a great fit for focusing on system logic and architecture."
}

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🔄 Prompt Style Comparison

Framework	Prompt Structure	Complexity	Readability
OpenAI SDK	messages=[...]	⭐⭐	❌
LangChain	ChatPromptTemplate	⭐⭐⭐⭐	⭐⭐
EasyLLM	Markdown + Jinja2	⭐	✅✅✅

✅ Recommended EasyLLM Style

from jinja2 import Template

prompt_t = Template("""
# System
You are a {{ domain }} assistant who helps users solve their problems.
# User
What is 1 + 1?
""")

response = llm(prompt_t.render(domain="math"))
print(response)

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🔧 Roadmap / TODO

• Built-in prompt versioning and template storage
• Visual Prompt Editor (Prompt Studio)
• Additional providers (e.g., Tongyi, Zhipu AI, Baichuan)

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❤️ Contributing

We welcome contributions from the community! You can:

• Open issues or submit PRs
• Suggest new prompt templates
• Help improve the documentation or examples

👉 Don’t forget to ⭐️ the repo to support its growth!

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📄 License

MIT License © 2025 [Chunyou Peng]

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📬 Contact

• Email: [e-mail]

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⭐️ Project Vision

"We aim to make EasyLLM the most intuitive tool for building Chinese LLM applications — writing prompts should feel as natural as writing Markdown."