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Decision brief
magicoder is a coding assistant tool that harnesses large language models to generate Python code snippets based on natural-language instruction input.
Good fit when
- Use magicoder if you need high-quality and accurate Python code generation for projects in need of robust APIs, such as the example TODO list application.
- Utilize magicoder when you require seamless integration with the transformers library from Hugging Face to generate text using large-scale language models.
Avoid when
- Do not use magicoder if your project requires code generation in languages other than Python, given it specializes in generating Python snippets.
- Avoid using this tool for situations where fine-grained control over the model parameters is critical; magicoder comes with specific settings that might limit customization options.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (641d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install magicoder PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Uses large language models to generate high-quality Python code from natural language instructions
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 5, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 5, 2026)
```python from transformers import pipelineSource link
Tags
README
🚀 Quick Start
from transformers import pipeline
import torch
MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
{instruction}
@@ Response
"""
instruction = "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."
prompt = MAGICODER_PROMPT.format(instruction=instruction)
generator = pipeline(
model="ise-uiuc/Magicoder-S-DS-6.7B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])
This code snippet will generate the following output:
Here is a simple Python implementation of a TODO list API:
```python
class TodoList:
def __init__(self):
self.todo_list = []
def add_task(self, task):
if not isinstance(task, str):
raise ValueError("Task must be a string")
self.todo_list.append(task)
def remove_task(self, task):
if task not in self.todo_list:
raise ValueError("Task not found in the list")
self.todo_list.remove(task)
def get_tasks(self):
return self.todo_list
def update_task(self, old_task, new_task):
if old_task not in self.todo_list:
raise ValueError("Old task not found in the list")
if not isinstance(new_task, str):
raise ValueError("New task must be a string")
index = self.todo_list.index(old_task)
self.todo_list[index] = new_task
def clear_list(self):
self.todo_list = []
```
This API allows you to add tasks, remove tasks, get all tasks, update tasks, and clear the list. It also raises exceptions for invalid operations.
You can use this API like this:
```python
todo = TodoList()
todo.add_task("Buy groceries")
todo.add_task("Finish project")
print(todo.get_tasks()) # Output: ['Buy groceries', 'Finish project']
todo.update_task("Buy groceries", "Buy fruits")
print(todo.get_tasks()) # Output: ['Buy fruits', 'Finish project']
todo.remove_task("Finish project")
print(todo.get_tasks()) # Output: ['Buy fruits']
todo.clear_list()
print(todo.get_tasks()) # Output: []
```
For agents
This page has a .md twin and JSON over the API.