Agent Testing Library

This repository provides a comprehensive framework for building, testing, and orchestrating agentic workflows in Python. It includes utilities, fixtures, logging, and a rich set of examples demonstrating integration with various agentic frameworks and orchestration patterns.

Project Structure

• agent_test/: Core testing library for agents

  • src/agent_utils/: Utilities for agent orchestration and metadata
    • remoterunnable_utils.py, models/agent_info.py, models/api_mock_type.py, models/global_metadata.py
  • common/: Logging and shared utilities
    • agent_test_logger.py
  • fixture/: Test fixtures and API mocks
    • fixture_class.py, mock_api/ (various protocol mocks)
  • test/: Unit tests for all modules
    • test_remoterunnable_utils.py, test_agent_info.py, test_api_mock_type.py, test_global_metadata.py, test_agent_test_logger.py, test_fixture_class.py

• examples/: Practical agentic workflow examples

  • common/: Shared example code
    • api1/api_code.py, worker1/main.py, worker2/main.py, worker3/main.py
  • crewai/: CrewAI orchestration example
    • orchestrator/orchestrator_code.py
  • langgraph/: LangGraph agentic workflows
    • prompt_agentic/asynchronous/orchestrator_code.py, prompt_agentic/asynchronous/test_orchestrator.py
    • prompt_agentic/synchronous/orchestrator_code.py, prompt_agentic/synchronous/test_orchestrator.py
    • simple_graph/orchestrator_code.py, simple_graph/test_orchestrator.py

Examples Section

The examples/ directory demonstrates how to build and orchestrate agentic workflows using different frameworks and patterns. Below are direct code snippets from key example files:

common/api1/api_code.py

def api_call(input_data):
	# Simulate API logic
	return {"result": f"Processed {input_data}"}

common/worker1/main.py

from common.api1.api_code import api_call

def worker1_task(data):
	response = api_call(data)
	print("Worker1 received:", response)

crewai/orchestrator/orchestrator_code.py

def orchestrate_agents(agent_list, task):
	results = []
	for agent in agent_list:
		result = agent.run(task)
		results.append(result)
	return results

langgraph/prompt_agentic/asynchronous/orchestrator_code.py

import asyncio

async def async_orchestrate(agents, task):
	tasks = [agent.run_async(task) for agent in agents]
	return await asyncio.gather(*tasks)

langgraph/prompt_agentic/synchronous/orchestrator_code.py

def sync_orchestrate(agents, task):
	return [agent.run(task) for agent in agents]

langgraph/simple_graph/orchestrator_code.py

def simple_graph_orchestrate(nodes, input_data):
	output = input_data
	for node in nodes:
		output = node.process(output)
	return output

Testing

Unit tests are provided in the agent_test/test/ directory. Run all tests with:

pytest agent_test/test

License

See LICENSE for details.

Future Work

Agentic Framework Alternatives (besides langgraph):

CrewAI (multi-agent orchestration) Autogen (Microsoft’s multi-agent framework) LangChain (chains, agents, tools) Haystack (for RAG and agent pipelines) OpenAI Function Calling (tool-using agents) Semantic Kernel (Microsoft’s orchestration) Custom agent frameworks (your own classes) HuggingFace Transformers Agents LlamaIndex (for agentic workflows) DSPy (for programmatic LLM pipelines) PromptChainer (open-source agentic framework) Direct function/callback-based agents