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A production-ready Python AI Agent engine using LangChain. Supports ReAct pattern, tool calling, and thinking process tracking.
Passed 22 of 22 test cases with the skill, and 11 of 22 without it.
and 15 more in the full report
11 fixed11 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Where should the agent engine class be imported from once the resource file has been placed into the standard project directory layout?
| Checked | Without skill | With skill |
|---|---|---|
| Imports AgentEngine from core.agent_engine. | Missed | Passed |
The instructions your model reads when the skill loads.
A plug-and-play AI Agent core for Python applications. It handles the complexity of LLM interaction, tool calling loops, and context management.
resources/agent_engine.py to your project (e.g., src/core/agent_engine.py).pip install langchain-core langchain-openai python-dotenv
.env file:
OPENAI_API_KEY=sk-...
# Optional:
OPENAI_BASE_URL=https://api.openai.com/v1
import asyncio
from langchain_core.tools import tool
from core.agent_engine import AgentEngine
# 1. Define Tools
@tool
def calculator(expression: str) -> str:
"""Calculates a math expression."""
return str(eval(expression))
# 2. Initialize Agent
agent = AgentEngine(
tools=[calculator],
system_prompt="You are a helpful math assistant.",
test cases passed, vs 11 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.