Add orchestrator test #1
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Caution Review failedThe pull request is closed. WalkthroughThis change introduces the foundational structure for the MCP Agent Stack project. It adds core agent orchestration logic, supporting Python packaging files, project documentation, and multiple GitHub Actions workflows for CI/CD, testing, release automation, and SBOM generation. A basic unit test, version tracking, and a Python dependency file are also included. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Orchestrator
participant LoadBalancer
participant DataParserAgent
participant SummarizerAgent
participant OptimizerAgent
participant LoggerAgent
User->>Orchestrator: run(data)
Orchestrator->>LoadBalancer: distribute(data)
LoadBalancer->>DataParserAgent: parse(data)
DataParserAgent-->>LoadBalancer: parsed_data
LoadBalancer->>LoggerAgent: log("Parsed data")
LoggerAgent-->>LoadBalancer: (logged)
LoadBalancer->>SummarizerAgent: summarize(content)
SummarizerAgent-->>LoadBalancer: summary
LoadBalancer->>LoggerAgent: log("Summarized data")
LoggerAgent-->>LoadBalancer: (logged)
LoadBalancer->>OptimizerAgent: optimize(summary)
OptimizerAgent-->>LoadBalancer: optimized_summary
LoadBalancer->>LoggerAgent: log("Optimized summary")
LoggerAgent-->>LoadBalancer: (logged)
LoadBalancer-->>Orchestrator: optimized_summary
Orchestrator-->>User: optimized_summary
Poem
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Summary of Changes
Hello @groupthinking, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request primarily sets up a new Python project for an 'MCP Agent Stack', establishing its basic structure, build configuration, and core agent-based orchestration logic. Concurrently, it introduces a unit test to validate the functionality of the newly implemented orchestrator, ensuring its workflow operates as intended.
Highlights
- Project Initialization and Setup: This pull request establishes the foundational structure for a new Python project, 'MCP Agent Stack'. This includes configuring
setuptoolsfor packaging (setup.py), defining project dependencies (requirements.txtforuuid), initializing project metadata (VERSION,CHANGELOG.md), and providing a comprehensiveREADME.mdwith project overview, setup, and deployment instructions. A.gitignorefile is also added to manage common Python-related ignored files. - Core Agent Orchestration System: Introduces the core logic for an agent-based orchestration system within
src/agents.py. This file defines several specialized agents:DataParserAgent,SummarizerAgent,OptimizerAgent, andLoggerAgent. It also includes aFactoryfor agent creation, aLoadBalancerto manage agent interactions, and anOrchestratorclass that coordinates the workflow of these agents to process input data. - Orchestrator Unit Test: Adds a dedicated unit test file,
src/test_orchestrator.py, using Python'sunittestframework. This test specifically targets theOrchestrator'srunmethod, verifying that it correctly processes input data through the defined agent workflow (parsing, summarizing, and optimizing) and produces the expected output string.
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Pull Request Overview
This PR introduces a basic unit test for the Orchestrator workflow and adds the core agent modules, along with CI/CD workflow stubs.
- Adds
TestOrchestratorcovering thatOrchestrator.runreturns a 50-character trimmed summary. - Implements agent classes (
DataParserAgent,SummarizerAgent,OptimizerAgent,LoggerAgent), factory, load balancer, and orchestrator. - Sets up GitHub Actions workflows for testing, SBOM stubs, and release stubs.
Reviewed Changes
Copilot reviewed 10 out of 12 changed files in this pull request and generated no comments.
Show a summary per file
| File | Description |
|---|---|
| src/test_orchestrator.py | New unit test verifying Orchestrator.run output |
| src/agents.py | Core agent implementations and orchestration logic |
| .github/workflows/test.yml | CI job for installing dependencies and running tests |
| README.md | Project overview and setup instructions |
| setup.py, VERSION, CHANGELOG.md | Package metadata and changelog stub |
| .github/workflows/sbom.yml | SBOM generation stub |
| .github/workflows/release.yml | Release pipeline stub |
Comments suppressed due to low confidence (3)
.github/workflows/test.yml:15
- The CI workflow references requirements.txt, but no such file exists in the repo. Please add a requirements.txt or update the workflow to skip dependency installation.
- run: pip install -r requirements.txt
README.md:7
- The README suggests installing dependencies via requirements.txt, but this file is missing. Consider adding requirements.txt or updating the setup instructions.
pip install -r requirements.txt
src/test_orchestrator.py:7
- [nitpick] Only one test case is provided; consider adding tests for shorter inputs (less than 50 chars) and inputs with leading/trailing whitespace to fully cover trimming behavior.
def test_run_returns_summary(self):
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Code Review
This pull request introduces a new agent orchestration system along with its initial project structure and a unit test. The overall structure is a good start. My review focuses on improving the robustness, maintainability, and testing practices. The most critical issue is the fragile way agents are handled by depending on their list order, which could lead to runtime errors. Other suggestions include using the standard logging module, cleaning up dependencies, and improving the test structure for better isolation and clarity.
| self.agents = agents | ||
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| def distribute(self, data: str) -> str: | ||
| parser, summarizer, optimizer, logger = self.agents |
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This positional unpacking of agents is very fragile. If the order of agents in the self.agents list changes (e.g., in Factory.create_agents), this code will assign the wrong agent instances to variables. This will lead to AttributeErrors at runtime that can be difficult to debug.
A more robust solution would be to use a dictionary to map agent roles to agent instances. This makes the dependencies explicit and prevents ordering issues.
For example:
# In Factory
def create_agents() -> dict[str, object]:
return {
"parser": DataParserAgent(),
"summarizer": SummarizerAgent(),
"optimizer": OptimizerAgent(),
"logger": LoggerAgent(),
}
# In LoadBalancer
# ...
parser = self.agents["parser"]
summarizer = self.agents["summarizer"]
# ...| @@ -0,0 +1 @@ | |||
| uuid | |||
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The uuid module is part of the Python standard library and does not need to be included in requirements.txt. It's available by default in all supported Python versions. Removing this line will clean up your dependencies.
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| setup( | ||
| name="mcp-agent-stack", | ||
| version=open("VERSION").read().strip(), |
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It's a best practice to use a with statement when opening files. This ensures that the file is automatically closed even if errors occur during reading. The current implementation could potentially leave a file descriptor open in case of an exception.
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| class LoggerAgent: | ||
| def log(self, message: str) -> None: | ||
| print(f"[LOG] {message}") |
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Using print() for logging is generally discouraged in application code because it's inflexible. You can't easily control the log level, format, or output destination (e.g., file vs. console).
Using Python's built-in logging module is a much better practice. You'll also need to add import logging at the top of the file.
| print(f"[LOG] {message}") | |
| logging.info(f"[LOG] {message}") |
| return [DataParserAgent(), SummarizerAgent(), OptimizerAgent(), LoggerAgent()] | ||
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| class LoadBalancer: |
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The class name LoadBalancer is a bit misleading. A load balancer typically distributes requests across multiple instances of the same service to improve performance and reliability. This class, however, seems to be orchestrating a sequential workflow or pipeline of different agents.
Consider renaming it to something that better reflects its purpose, such as AgentPipeline or WorkflowManager, to improve code clarity.
| orchestrator = Orchestrator() | ||
| result = orchestrator.run(data) |
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This test can be improved in a couple of ways for better structure and isolation:
- Test Fixture: The
Orchestratorinstance is created inside the test method. It's a common practice to move object creation to asetUpmethod. This avoids repetition if you add more test methods to this class. - Log Capturing: The
LoggerAgentcurrently prints to standard output, which will pollute the test run logs. It's better to mock the logger during tests to prevent this and to optionally assert that it was called correctly.
Here is an example incorporating these ideas:
import unittest
from unittest.mock import patch
from agents import Orchestrator
class TestOrchestrator(unittest.TestCase):
def setUp(self):
self.orchestrator = Orchestrator()
@patch('agents.LoggerAgent.log')
def test_run_returns_summary(self, mock_log):
data = "This is a test input string that should be summarized and optimized."
result = self.orchestrator.run(data)
self.assertIsInstance(result, str)
expected = data.strip()[:50]
self.assertEqual(result, expected)
self.assertTrue(mock_log.called)
Summary
Testing
python -m unittest discover srchttps://chatgpt.com/codex/tasks/task_e_686ef3e6c1048323986ad97a9f5a6fe9
Summary by CodeRabbit
New Features
Documentation
Tests
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