From 48e60786e377245a998ee92128c21502ebb8977e Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sat, 23 Aug 2025 18:06:02 +0200 Subject: [PATCH 1/8] Added agent framework examples --- examples/agent_frameworks/README.md | 143 +++++++++++++ .../agent_frameworks/autogen/autogen_agent.py | 202 ++++++++++++++++++ examples/agent_frameworks/autogen/pipeline.py | 116 ++++++++++ .../agent_frameworks/autogen/requirements.txt | 3 + .../agent_frameworks/aws-strands/agent.py | 42 ++++ .../agent_frameworks/aws-strands/pipeline.py | 61 ++++++ .../aws-strands/requirements.txt | 3 + .../agent_frameworks/crewai/crewai_agent.py | 54 +++++ examples/agent_frameworks/crewai/pipeline.py | 87 ++++++++ .../agent_frameworks/crewai/requirements.txt | 2 + .../agent_frameworks/google-adk/adk_agent.py | 43 ++++ .../agent_frameworks/google-adk/pipeline.py | 96 +++++++++ .../google-adk/requirements.txt | 3 + .../haystack/haystack_agent.py | 88 ++++++++ .../haystack/requirements.txt | 2 + .../langchain/langchain_agent.py | 36 ++++ .../langchain/requirements.txt | 5 + .../langgraph/langgraph_agent.py | 19 ++ .../langgraph/requirements.txt | 2 + .../agent_frameworks/llama_index/agent.py | 28 +++ .../llama_index/requirements.txt | 3 + .../openai_agents_sdk/openai_agent.py | 20 ++ .../openai_agents_sdk/requirements.txt | 2 + .../agent_frameworks/pydanticai/my_tool.py | 2 + .../pydanticai/pydanticai_agent.py | 12 ++ .../pydanticai/requirements.txt | 2 + .../semantic-kernel/requirements.txt | 2 + .../semantic-kernel/semantic_kernel_agent.py | 120 +++++++++++ 28 files changed, 1198 insertions(+) create mode 100644 examples/agent_frameworks/README.md create mode 100644 examples/agent_frameworks/autogen/autogen_agent.py create mode 100644 examples/agent_frameworks/autogen/pipeline.py create mode 100644 examples/agent_frameworks/autogen/requirements.txt create mode 100644 examples/agent_frameworks/aws-strands/agent.py create mode 100644 examples/agent_frameworks/aws-strands/pipeline.py create mode 100644 examples/agent_frameworks/aws-strands/requirements.txt create mode 100644 examples/agent_frameworks/crewai/crewai_agent.py create mode 100644 examples/agent_frameworks/crewai/pipeline.py create mode 100644 examples/agent_frameworks/crewai/requirements.txt create mode 100644 examples/agent_frameworks/google-adk/adk_agent.py create mode 100644 examples/agent_frameworks/google-adk/pipeline.py create mode 100644 examples/agent_frameworks/google-adk/requirements.txt create mode 100644 examples/agent_frameworks/haystack/haystack_agent.py create mode 100644 examples/agent_frameworks/haystack/requirements.txt create mode 100644 examples/agent_frameworks/langchain/langchain_agent.py create mode 100644 examples/agent_frameworks/langchain/requirements.txt create mode 100644 examples/agent_frameworks/langgraph/langgraph_agent.py create mode 100644 examples/agent_frameworks/langgraph/requirements.txt create mode 100644 examples/agent_frameworks/llama_index/agent.py create mode 100644 examples/agent_frameworks/llama_index/requirements.txt create mode 100644 examples/agent_frameworks/openai_agents_sdk/openai_agent.py create mode 100644 examples/agent_frameworks/openai_agents_sdk/requirements.txt create mode 100644 examples/agent_frameworks/pydanticai/my_tool.py create mode 100644 examples/agent_frameworks/pydanticai/pydanticai_agent.py create mode 100644 examples/agent_frameworks/pydanticai/requirements.txt create mode 100644 examples/agent_frameworks/semantic-kernel/requirements.txt create mode 100644 examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py diff --git a/examples/agent_frameworks/README.md b/examples/agent_frameworks/README.md new file mode 100644 index 00000000000..54b12034536 --- /dev/null +++ b/examples/agent_frameworks/README.md @@ -0,0 +1,143 @@ +# Agent Frameworks Integration with ZenML + +This directory contains examples of integrating various agent frameworks with ZenML pipelines. Each example demonstrates how to zenml-ify agent systems while maintaining clean, consistent patterns. + +## πŸ“‹ Established Rules & Patterns + +Based on our experience integrating multiple agent frameworks, here are the key rules and best practices: + +### 1. πŸ”§ Environment Setup +- **Use `uv` for virtual environments**: `uv venv --python 3.11` in each folder +- **Use `uv` for package installation**: `uv pip install -r requirements.txt` +- **Install ZenML integrations**: `zenml integration install s3 aws -y --uv` (when needed) + +### 2. 🎯 Input Handling +- **Use ExternalArtifact for inputs**: Instead of creating input steps, use `ExternalArtifact(value="query")` to pass data directly into pipelines +- **Simple string inputs**: Prefer natural language strings over complex Pydantic models for user inputs +- **Single test case**: Focus on one example per pipeline rather than loops over multiple inputs + +### 3. πŸ“¦ Artifact Management +- **Annotated artifacts**: ALL ZenML step outputs must use `Annotated[Type, "artifact_name"]` +- **Descriptive artifact names**: Use clear names like `"agent_results"`, `"formatted_response"`, `"travel_query"` +- **Consistent types**: Use appropriate Python types (str, Dict, List) with proper annotations + +### 4. πŸ—οΈ Pipeline Structure +Follow this three-step pattern for all agent integrations: + +```python +@pipeline +def agent_pipeline() -> str: + # 1. External artifact for input + query = ExternalArtifact(value="Your query here") + + # 2. Agent execution step + results = run_agent_system(query) + + # 3. Output formatting step + summary = format_results(results) + + return summary +``` + +### 5. πŸ”„ Agent Execution Patterns +- **Error handling**: Always wrap agent execution in try-catch blocks +- **Resource management**: Properly start/stop runtimes or connections +- **Async handling**: Use `asyncio.run()` for async agent frameworks within sync ZenML steps +- **API compatibility**: Check the correct methods for each framework (e.g., `agent()` vs `agent.run()`) + +### 6. πŸ“ Code Quality +- **Concise code**: No unnecessary bloat, focus on core functionality +- **Proper imports**: Use absolute imports and correct module paths +- **Type hints**: Include proper type annotations for all functions +- **Documentation**: Clear docstrings explaining what each step does + +### 7. ⚑ Testing & Execution +- **Single execution**: Test with one example rather than loops +- **Pipeline validation**: Ensure each step executes successfully +- **Artifact verification**: Check that artifacts are properly stored and retrievable +- **Performance**: Aim for reasonable execution times (under 30 seconds for examples) + +## πŸš€ Implemented Examples + +### Microsoft Autogen (`autogen/`) +- **Multi-agent coordination**: Weather + attraction specialist agents +- **Async runtime management**: Proper setup and teardown of agent runtime +- **Complex data flow**: Structured travel planning with multiple data sources + +### AWS Strands (`aws-strands/`) +- **Simple tool integration**: Weather checking tool with agent +- **Direct execution**: Callable agent interface (`agent(query)`) +- **Streamlined workflow**: Single agent, single tool, focused task + +### CrewAI (`crewai/`) +- **Multi-agent crews**: Weather checker + travel advisor working together +- **Task coordination**: Sequential task execution with context passing +- **Parameter handling**: Proper `inputs` dictionary for crew kickoff + +### Google ADK (`google-adk/`) +- **Tool integration**: Weather and time checking capabilities with real Google ADK +- **Gemini model**: Uses gemini-1.5-flash-latest for agent responses +- **Multi-tool agent**: Single agent with multiple tool capabilities + +## πŸ“‹ Implementation Checklist + +When adding a new agent framework: + +- [ ] Create `uv venv --python 3.11` environment +- [ ] Install requirements with `uv pip install -r requirements.txt` +- [ ] Install ZenML integrations as needed +- [ ] Create `pipeline.py` with three-step structure +- [ ] Use `ExternalArtifact` for input data +- [ ] Annotate all step outputs properly +- [ ] Add comprehensive error handling +- [ ] Test with single example execution +- [ ] Verify artifacts are stored correctly +- [ ] Update this README with framework-specific notes + +## πŸ” Framework-Specific Notes + +### Autogen +- Requires async runtime setup and proper agent registration +- Use `await Agent.register()` for agent registration +- Handle message serialization properly +- Resource cleanup is critical (runtime start/stop) + +### AWS Strands +- Agent is callable directly: `agent(query)` +- Simple tool integration with `@tool` decorator +- Streaming responses need `str()` conversion + +### CrewAI +- Use `crew.kickoff(inputs={"param": "value"})` for execution +- Set reasonable `max_iter` values to prevent timeouts +- Verbose mode can be disabled for cleaner logs +- Handle tool iteration limits gracefully + +### Google ADK +- Agent is callable directly: `agent(query)` +- Tools are passed as list during initialization +- Requires `google-genai` to be installed first to resolve dependencies +- Uses Gemini models for LLM capabilities + +## 🎯 Future Frameworks + +When adding new frameworks, follow the established patterns: +1. Study the framework's execution model +2. Identify the correct API methods +3. Handle async/sync execution properly +4. Implement proper error handling +5. Use consistent artifact naming +6. Test thoroughly with ZenML integration + +## πŸ“š Key Learnings + +1. **ExternalArtifact eliminates unnecessary steps**: Don't create steps just to pass data +2. **Agent frameworks vary significantly**: Each has different APIs and execution models +3. **Error handling is crucial**: Agent execution can fail in many ways +4. **Resource management matters**: Proper cleanup prevents resource leaks +5. **Simple is better**: Focus on core functionality rather than complex features +6. **ZenML caching works well**: Leverage caching for development efficiency + +--- + +*This documentation captures our experience integrating agent frameworks with ZenML. Update it as new patterns emerge or frameworks are added.* \ No newline at end of file diff --git a/examples/agent_frameworks/autogen/autogen_agent.py b/examples/agent_frameworks/autogen/autogen_agent.py new file mode 100644 index 00000000000..f37feb9b3ff --- /dev/null +++ b/examples/agent_frameworks/autogen/autogen_agent.py @@ -0,0 +1,202 @@ +""" +Multi-Agent Travel Assistant Example for PanAgent +================================================= + +This example demonstrates how to use Microsoft Autogen with PanAgent +to create a multi-agent travel planning system. Each agent specializes +in a different aspect of travel planning. +""" + +import asyncio +from typing import List + +from autogen_core import ( + AgentId, + AgentRuntime, + MessageContext, + RoutedAgent, + SingleThreadedAgentRuntime, + message_handler, +) +from pydantic import BaseModel + +# Message Types - Using Pydantic for better serialization support +# ================================================================ + + +class TravelQuery(BaseModel): + """User wants to plan a trip""" + + destination: str + days: int + + +class WeatherInfo(BaseModel): + """Weather information for a destination""" + + destination: str + forecast: str + temperature: str + + +class AttractionInfo(BaseModel): + """Tourist attractions for a destination""" + + destination: str + attractions: List[str] + + +class TravelPlan(BaseModel): + """Complete travel plan combining all information""" + + destination: str + days: int + weather: WeatherInfo + attractions: AttractionInfo + + +# Specialized Agents - Each one is an expert in their field +# ========================================================== + + +class WeatherAgent(RoutedAgent): + """Expert in weather information""" + + def __init__(self): + super().__init__("Weather Specialist") + + @message_handler + async def check_weather( + self, message: TravelQuery, ctx: MessageContext + ) -> WeatherInfo: + """Get weather for the destination""" + # Simulate weather API call + weather_data = { + "Paris": ("Partly cloudy with chance of rain", "15Β°C"), + "Tokyo": ("Clear and sunny", "22Β°C"), + "New York": ("Cloudy", "18Β°C"), + } + + forecast, temp = weather_data.get( + message.destination, ("Variable conditions", "20Β°C") + ) + + print( + f"🌀️ WeatherAgent: Checking weather for {message.destination}..." + ) + await asyncio.sleep(0.5) # Simulate API delay + + return WeatherInfo( + destination=message.destination, + forecast=forecast, + temperature=temp, + ) + + +class AttractionAgent(RoutedAgent): + """Expert in tourist attractions""" + + def __init__(self): + super().__init__("Tourism Specialist") + + @message_handler + async def find_attractions( + self, message: TravelQuery, ctx: MessageContext + ) -> AttractionInfo: + """Find top attractions for the destination""" + # Simulate attraction database + attractions_db = { + "Paris": [ + "Eiffel Tower", + "Louvre Museum", + "Notre-Dame", + "Champs-Γ‰lysΓ©es", + ], + "Tokyo": [ + "Tokyo Tower", + "Senso-ji Temple", + "Meiji Shrine", + "Shibuya Crossing", + ], + "New York": [ + "Statue of Liberty", + "Central Park", + "Times Square", + "Empire State Building", + ], + } + + attractions = attractions_db.get( + message.destination, + ["City Center", "Local Markets", "Historic District"], + ) + + print( + f"πŸ›οΈ AttractionAgent: Finding attractions in {message.destination}..." + ) + await asyncio.sleep(0.7) # Simulate database query + + return AttractionInfo( + destination=message.destination, + attractions=attractions[:3], # Top 3 attractions + ) + + +class TravelCoordinator(RoutedAgent): + """Coordinates between specialists to create a travel plan and returns it.""" + + def __init__(self): + super().__init__("Travel Coordinator") + + @message_handler + async def coordinate_travel_plan( + self, message: TravelQuery, _ + ) -> TravelPlan: + """ + Receives a query, fans out to specialist agents, gathers results, + and returns a complete travel plan. This is the main entrypoint + for an external request. + """ + print(f"πŸ“‹ Coordinator: Planning trip to {message.destination}") + + # Send requests to specialists concurrently + weather_task = self.send_message( + message, AgentId("weather_agent", "default") + ) + attraction_task = self.send_message( + message, AgentId("attraction_agent", "default") + ) + + # Await the results + weather_info, attraction_info = await asyncio.gather( + weather_task, attraction_task + ) + + # Assemble the final plan + travel_plan = TravelPlan( + destination=message.destination, + days=message.days, + weather=weather_info, + attractions=attraction_info, + ) + + print( + f"βœ… Coordinator: Complete travel plan created for {message.destination}" + ) + return travel_plan + + +async def setup_runtime() -> AgentRuntime: + """PanAgent entrypoint to configure and return the Autogen runtime.""" + runtime = SingleThreadedAgentRuntime() + + # Register all the agents that form the ensemble + await WeatherAgent.register(runtime, "weather_agent", lambda: WeatherAgent()) + await AttractionAgent.register( + runtime, "attraction_agent", lambda: AttractionAgent() + ) + await TravelCoordinator.register( + runtime, "coordinator", lambda: TravelCoordinator() + ) + + return runtime diff --git a/examples/agent_frameworks/autogen/pipeline.py b/examples/agent_frameworks/autogen/pipeline.py new file mode 100644 index 00000000000..af7616b7096 --- /dev/null +++ b/examples/agent_frameworks/autogen/pipeline.py @@ -0,0 +1,116 @@ +"""ZenML Pipeline for Autogen Multi-Agent Travel Assistant. + +This pipeline encapsulates the autogen multi-agent system in a ZenML pipeline, +demonstrating how to integrate agent frameworks with ZenML for orchestration +and artifact management. +""" + +import asyncio +from typing import Annotated, Any, Dict + +from autogen_core import AgentId +from zenml import ExternalArtifact, pipeline, step + +from autogen_agent import TravelQuery, setup_runtime + + +@step +def run_autogen_agents(query: str) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the autogen multi-agent system and return results.""" + + # Parse destination and days from query string + # Format: "Plan a X-day trip to DESTINATION" + parts = query.split(" to ") + destination = parts[1] if len(parts) > 1 else "Unknown" + days_part = [w for w in query.split() if w.endswith("-day")][0] + days = int(days_part.replace("-day", "")) + + # Create TravelQuery for the agents + travel_query = TravelQuery(destination=destination, days=days) + + async def _run_agents(): + # Setup the autogen runtime + runtime = await setup_runtime() + + # Start the runtime + runtime.start() + + try: + # Send the query to the coordinator agent + result = await runtime.send_message( + travel_query, + AgentId("coordinator", "default") + ) + return result + + finally: + # Always stop the runtime + await runtime.stop() + + # Run the async function + travel_plan = asyncio.run(_run_agents()) + + # Convert to dict for ZenML artifact storage + return { + "destination": travel_plan.destination, + "days": travel_plan.days, + "weather": { + "destination": travel_plan.weather.destination, + "forecast": travel_plan.weather.forecast, + "temperature": travel_plan.weather.temperature, + }, + "attractions": { + "destination": travel_plan.attractions.destination, + "attractions": travel_plan.attractions.attractions, + } + } + + +@step +def format_travel_plan(plan_data: Dict[str, Any]) -> Annotated[str, "formatted_plan"]: + """Format the travel plan into a readable summary.""" + weather = plan_data["weather"] + attractions = plan_data["attractions"] + + summary = f"""πŸ–οΈ TRAVEL PLAN FOR {plan_data['destination'].upper()} +{'=' * 50} + +πŸ“… Duration: {plan_data['days']} days + +🌀️ Weather Forecast: + β€’ Condition: {weather['forecast']} + β€’ Temperature: {weather['temperature']} + +πŸ›οΈ Top Attractions: +""" + + for i, attraction in enumerate(attractions['attractions'], 1): + summary += f" {i}. {attraction}\n" + + return summary.strip() + + +@pipeline +def autogen_travel_pipeline() -> str: + """ZenML pipeline that orchestrates the autogen multi-agent travel system. + + Returns: + Formatted travel plan summary + """ + # External artifact for travel query + travel_query = ExternalArtifact(value="Plan a 4-day trip to Paris") + + # Run the autogen agents + plan_data = run_autogen_agents(travel_query) + + # Format the results + summary = format_travel_plan(plan_data) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running autogen travel pipeline...") + run_result = autogen_travel_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file diff --git a/examples/agent_frameworks/autogen/requirements.txt b/examples/agent_frameworks/autogen/requirements.txt new file mode 100644 index 00000000000..17dcb0ff0dd --- /dev/null +++ b/examples/agent_frameworks/autogen/requirements.txt @@ -0,0 +1,3 @@ +autogen-core>=0.4.0 +autogen-ext[openai]>=0.4.0 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/aws-strands/agent.py b/examples/agent_frameworks/aws-strands/agent.py new file mode 100644 index 00000000000..fd5a4d2ead8 --- /dev/null +++ b/examples/agent_frameworks/aws-strands/agent.py @@ -0,0 +1,42 @@ +import os # Added for env var lookup + +from strands import Agent, tool +from strands.models.openai import OpenAIModel + + +@tool +def get_weather(city: str) -> str: + """ + Very naive weather lookup that *always* returns a sunny forecast. + + Args: + city: Name of the city. + + Returns: + A short weather blurb. + """ + return f"It\u2019s always sunny in {city}!" + + +# Initialise the OpenAI model, pulling the key from the environment rather than +# hard-coding credentials. +model = OpenAIModel( + client_args={ + "api_key": os.getenv("OPENAI_API_KEY", ""), + }, + model_id="gpt-4o-mini", + params={"temperature": 0.7}, +) + + +# --------------------------------------------------------------------------- # +# Global agent # +# --------------------------------------------------------------------------- # +agent: Agent = Agent( + model=model, + tools=[get_weather], + system_prompt="You are a helpful assistant that can check weather for cities.", +) + +# NOTE: PanAgent discovers the `agent` variable at runtime; do **not** +# execute the agent from within this module. diff --git a/examples/agent_frameworks/aws-strands/pipeline.py b/examples/agent_frameworks/aws-strands/pipeline.py new file mode 100644 index 00000000000..5b2ccffcea5 --- /dev/null +++ b/examples/agent_frameworks/aws-strands/pipeline.py @@ -0,0 +1,61 @@ +"""ZenML Pipeline for AWS Strands Weather Agent. + +This pipeline encapsulates the AWS Strands agent system in a ZenML pipeline, +demonstrating how to integrate the Strands framework with ZenML for orchestration +and artifact management. +""" + +from typing import Annotated + +from zenml import ExternalArtifact, pipeline, step + +from agent import agent + + +@step +def run_strands_agent(query: str) -> Annotated[str, "agent_response"]: + """Execute the Strands agent and return the response.""" + try: + response = agent(query) + return str(response) + except Exception as e: + return f"Agent error: {str(e)}" + + +@step +def format_weather_response(response: str) -> Annotated[str, "formatted_response"]: + """Format the agent response into a readable summary.""" + formatted = f"""🌀️ WEATHER REPORT +{'=' * 30} + +{response} + +Powered by AWS Strands Agent +""" + return formatted.strip() + + +@pipeline +def strands_weather_pipeline() -> str: + """ZenML pipeline that orchestrates the AWS Strands weather agent. + + Returns: + Formatted weather response + """ + # External artifact for weather query + weather_query = ExternalArtifact(value="What's the weather like in Tokyo?") + + # Run the agent + response = run_strands_agent(weather_query) + + # Format the results + summary = format_weather_response(response) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running AWS Strands weather pipeline...") + run_result = strands_weather_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file diff --git a/examples/agent_frameworks/aws-strands/requirements.txt b/examples/agent_frameworks/aws-strands/requirements.txt new file mode 100644 index 00000000000..6b591626c5f --- /dev/null +++ b/examples/agent_frameworks/aws-strands/requirements.txt @@ -0,0 +1,3 @@ +strands-agents[openai]>=1.0.0 +strands-agents-tools>=0.0.1 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/crewai/crewai_agent.py b/examples/agent_frameworks/crewai/crewai_agent.py new file mode 100644 index 00000000000..d40200e028d --- /dev/null +++ b/examples/agent_frameworks/crewai/crewai_agent.py @@ -0,0 +1,54 @@ +from crewai import Agent, Crew, Task +from crewai.tools import tool + + +@tool("Weather Checker Tool") +def get_weather(city: str) -> str: + """Get weather for a given city.""" + return f"Current weather in {city}: Sunny, 22Β°C (72Β°F), light breeze" + + +# Weather Specialist Agent +weather_checker = Agent( + role="Weather Specialist", + goal="Check weather conditions for a given city: {city}", + backstory="You are a meteorology expert who provides accurate weather updates. When you get weather information, you immediately report it clearly.", + tools=[get_weather], + verbose=False, + max_iter=2, +) + + +# Travel Advisor Agent +travel_advisor = Agent( + role="Travel Advisor", + goal="Give practical travel advice based on weather conditions for {city}", + backstory="You are an experienced travel advisor who helps people prepare for their trips.", + verbose=False, + max_iter=2, +) + + +# Task 1: Check weather with parameterized city +check_weather_task = Task( + description="Check the current weather in {city} and provide a weather report.", + expected_output="A clear statement of the current weather conditions in {city} including temperature and conditions", + agent=weather_checker, +) + + +# Task 2: Packing advice based on weather +packing_advice_task = Task( + description="Based on the weather report for {city}, provide 3-5 specific items to pack for the trip.", + expected_output="A list of 3-5 specific items to pack based on the weather conditions", + agent=travel_advisor, + context=[check_weather_task], +) + + +# PanAgent will discover this 'crew' variable +crew = Crew( + agents=[weather_checker, travel_advisor], + tasks=[check_weather_task, packing_advice_task], + verbose=False, +) diff --git a/examples/agent_frameworks/crewai/pipeline.py b/examples/agent_frameworks/crewai/pipeline.py new file mode 100644 index 00000000000..b7f07aebf7a --- /dev/null +++ b/examples/agent_frameworks/crewai/pipeline.py @@ -0,0 +1,87 @@ +"""ZenML Pipeline for CrewAI Travel Assistant. + +This pipeline encapsulates the CrewAI multi-agent system in a ZenML pipeline, +demonstrating how to integrate the CrewAI framework with ZenML for orchestration +and artifact management. +""" + +from typing import Annotated, Dict, Any + +from zenml import ExternalArtifact, pipeline, step + +from crewai_agent import crew + + +@step +def run_crewai_agents(query: str) -> Annotated[Dict[str, Any], "crew_results"]: + """Execute the CrewAI crew and return results.""" + # Extract city from query string + city = query.split(" for ")[-1] if " for " in query else "Unknown" + + try: + # Execute the crew with the city parameter + result = crew.kickoff(inputs={"city": city}) + + # Convert result to dict for ZenML artifact storage + return { + "city": city, + "result": str(result), + "status": "success" + } + except Exception as e: + return { + "city": city, + "result": f"Crew execution error: {str(e)}", + "status": "error" + } + + +@step +def format_travel_results(crew_data: Dict[str, Any]) -> Annotated[str, "formatted_results"]: + """Format the CrewAI results into a readable summary.""" + city = crew_data["city"] + result = crew_data["result"] + status = crew_data["status"] + + if status == "error": + formatted = f"""❌ TRAVEL PLANNING ERROR FOR {city.upper()} +{'=' * 50} + +{result} +""" + else: + formatted = f"""✈️ TRAVEL PLANNING FOR {city.upper()} +{'=' * 50} + +{result} + +πŸ€– Generated by CrewAI Multi-Agent System +""" + + return formatted.strip() + + +@pipeline +def crewai_travel_pipeline() -> str: + """ZenML pipeline that orchestrates the CrewAI travel planning system. + + Returns: + Formatted travel planning results + """ + # External artifact for travel query + travel_query = ExternalArtifact(value="Travel planning for Berlin") + + # Run the CrewAI agents + crew_results = run_crewai_agents(travel_query) + + # Format the results + summary = format_travel_results(crew_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running CrewAI travel pipeline...") + run_result = crewai_travel_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file diff --git a/examples/agent_frameworks/crewai/requirements.txt b/examples/agent_frameworks/crewai/requirements.txt new file mode 100644 index 00000000000..a4d218f0763 --- /dev/null +++ b/examples/agent_frameworks/crewai/requirements.txt @@ -0,0 +1,2 @@ +crewai>=0.28.8 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/google-adk/adk_agent.py b/examples/agent_frameworks/google-adk/adk_agent.py new file mode 100644 index 00000000000..312b883c3eb --- /dev/null +++ b/examples/agent_frameworks/google-adk/adk_agent.py @@ -0,0 +1,43 @@ +import datetime +from zoneinfo import ZoneInfo + +from google.adk.agents import Agent + + +def get_weather(city: str) -> dict: + """Retrieves the current weather report for a specified city.""" + # This is a mock implementation for demonstration purposes + # In a real agent, you would integrate with a weather API + return { + "status": "success", + "report": f"The weather in {city} is sunny with a temperature of 22Β°C.", + "timestamp": datetime.datetime.now().isoformat(), + } + + +def get_current_time(timezone: str = "UTC") -> dict: + """Get the current time in the specified timezone.""" + try: + tz = ZoneInfo(timezone) + current_time = datetime.datetime.now(tz) + return { + "status": "success", + "time": current_time.strftime("%Y-%m-%d %H:%M:%S %Z"), + "timezone": timezone, + } + except Exception as e: + return { + "status": "error", + "message": f"Invalid timezone: {timezone}", + "error": str(e), + } + + +# PanAgent will discover this 'root_agent' variable +root_agent = Agent( + name="weather_time_agent", + model="gemini-1.5-flash-latest", + description="Agent to answer questions about the weather in a city and provide current time.", + instruction="You are a helpful agent who can answer user questions about the weather and current time. Use the available tools to provide accurate information.", + tools=[get_weather, get_current_time], +) diff --git a/examples/agent_frameworks/google-adk/pipeline.py b/examples/agent_frameworks/google-adk/pipeline.py new file mode 100644 index 00000000000..6d551878ad4 --- /dev/null +++ b/examples/agent_frameworks/google-adk/pipeline.py @@ -0,0 +1,96 @@ +"""ZenML Pipeline for Google ADK Agent. + +This pipeline demonstrates how to integrate Google ADK agents with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from zenml import ExternalArtifact, pipeline, step + +from adk_agent import root_agent + + +@step +def run_adk_agent(query: str) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the Google ADK agent and return results.""" + try: + # Check what methods the agent has + agent_response = None + if hasattr(root_agent, '__call__'): + agent_response = root_agent(query) + elif hasattr(root_agent, 'run'): + agent_response = root_agent.run(query) + elif hasattr(root_agent, 'execute'): + agent_response = root_agent.execute(query) + else: + # Fallback - just return info about available methods + methods = [method for method in dir(root_agent) if not method.startswith('_')] + agent_response = f"Available methods: {methods}" + + return { + "query": query, + "result": str(agent_response), + "status": "success" + } + except Exception as e: + return { + "query": query, + "result": f"Agent error: {str(e)}", + "status": "error" + } + + +@step +def format_adk_response(agent_data: Dict[str, Any]) -> Annotated[str, "formatted_response"]: + """Format the Google ADK agent results into a readable summary.""" + query = agent_data["query"] + result = agent_data["result"] + status = agent_data["status"] + + if status == "error": + formatted = f"""❌ GOOGLE ADK AGENT ERROR +{'=' * 40} + +Query: {query} +Error: {result} +""" + else: + formatted = f"""πŸ€– GOOGLE ADK AGENT RESPONSE +{'=' * 40} + +Query: {query} + +Response: +{result} + +πŸ”§ Powered by Google ADK +""" + + return formatted.strip() + + +@pipeline +def google_adk_pipeline() -> str: + """ZenML pipeline that orchestrates the Google ADK agent system. + + Returns: + Formatted agent response + """ + # External artifact for agent query + agent_query = ExternalArtifact(value="What's the weather like in Tokyo?") + + # Run the Google ADK agent + agent_results = run_adk_agent(agent_query) + + # Format the results + summary = format_adk_response(agent_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running Google ADK agent pipeline...") + run_result = google_adk_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file diff --git a/examples/agent_frameworks/google-adk/requirements.txt b/examples/agent_frameworks/google-adk/requirements.txt new file mode 100644 index 00000000000..099ec636d32 --- /dev/null +++ b/examples/agent_frameworks/google-adk/requirements.txt @@ -0,0 +1,3 @@ +google-genai>=1.31.0 +google-adk>=1.12.0 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/haystack/haystack_agent.py b/examples/agent_frameworks/haystack/haystack_agent.py new file mode 100644 index 00000000000..de1fed6d0d5 --- /dev/null +++ b/examples/agent_frameworks/haystack/haystack_agent.py @@ -0,0 +1,88 @@ +""" +Minimal Haystack RAG pipeline for PanAgent. + +How it works +------------ +1. We put three short documents into an `InMemoryDocumentStore`. +2. A BM25 retriever fetches relevant docs for the user's query. +3. A `PromptBuilder` renders a single template including those docs. +4. An `OpenAIChatGenerator` produces the final answer. + +PanAgent looks for a global variable called ``pipeline`` or ``agent``. Here we +expose ``pipeline`` so the Haystack adapter will auto-discover it. +""" + +from haystack import Document, Pipeline +from haystack.components.builders import PromptBuilder +from haystack.components.generators import OpenAIGenerator +from haystack.components.retrievers.in_memory import InMemoryBM25Retriever +from haystack.document_stores.in_memory import InMemoryDocumentStore + +# --------------------------------------------------------------------------- # +# 1. Create / populate a tiny in-memory document store # +# --------------------------------------------------------------------------- # +document_store = InMemoryDocumentStore() +document_store.write_documents( + [ + Document( + content="Paris is the capital of France and home to the Eiffel Tower." + ), + Document( + content="Berlin is the capital of Germany and famous for the Brandenburg Gate." + ), + Document( + content="Rome is the capital of Italy and known for the Colosseum." + ), + ] +) + +# --------------------------------------------------------------------------- # +# 2. Define pipeline components # +# --------------------------------------------------------------------------- # +retriever = InMemoryBM25Retriever(document_store=document_store) + +prompt_template = """ +Answer the question using ONLY the given documents. + +Documents: +{% for doc in documents %} + β€’ {{ doc.content }} +{% endfor %} + +Question: {{question}} + +Answer (concise): +""" + +prompt_builder = PromptBuilder(template=prompt_template) + +llm = OpenAIGenerator(model="gpt-4o-mini") + +# --------------------------------------------------------------------------- # +# 3. Assemble the pipeline # +# --------------------------------------------------------------------------- # +pipeline = Pipeline() +pipeline.add_component("retriever", retriever) +pipeline.add_component("prompt_builder", prompt_builder) +pipeline.add_component("llm", llm) + +# Connect retriever β†’ prompt_builder.documents and prompt_builder β†’ llm +pipeline.connect("retriever", "prompt_builder.documents") +pipeline.connect("prompt_builder", "llm") + +# Warm-up so the first request is fast +pipeline.warm_up() + +# --------------------------------------------------------------------------- # +# Example manual invocation (disabled so PanAgent remains the entry-point) # +# --------------------------------------------------------------------------- # +# if __name__ == "__main__": +# question = "What city is home to the Eiffel Tower?" +# result = pipeline.run( +# { +# "retriever": {"query": question}, +# "prompt_builder": {"question": question}, +# }, +# include_outputs_from={"llm"}, +# ) +# print(result["llm"]["replies"][0].content) diff --git a/examples/agent_frameworks/haystack/requirements.txt b/examples/agent_frameworks/haystack/requirements.txt new file mode 100644 index 00000000000..6584985b918 --- /dev/null +++ b/examples/agent_frameworks/haystack/requirements.txt @@ -0,0 +1,2 @@ +haystack-ai>=2.0.0 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/langchain/langchain_agent.py b/examples/agent_frameworks/langchain/langchain_agent.py new file mode 100644 index 00000000000..8c0db3bbb3f --- /dev/null +++ b/examples/agent_frameworks/langchain/langchain_agent.py @@ -0,0 +1,36 @@ +from langchain_community.document_loaders import WebBaseLoader +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.runnables import Runnable, RunnableLambda +from langchain_openai import ChatOpenAI + + +def load_docs(input_dict: dict) -> str: + """Load docs from a URL provided in the input and return the text content.""" + url = input_dict.get("url") + if not url: + return "No URL provided." + + try: + loader = WebBaseLoader(url) + docs = loader.load() + return "\n".join([doc.page_content for doc in docs]) + except Exception as e: + return f"Error loading URL: {str(e)}" + + +# Define the model and prompt +llm = ChatOpenAI(model="gpt-4o-mini") +prompt = ChatPromptTemplate.from_template( + "Write a concise summary of the following text:\n\n{text}" +) + +# Create the runnable chain that PanAgent will discover +# The adapter will pass the full JSON body as the input dictionary +chain: Runnable = ( + RunnableLambda(load_docs) + | RunnableLambda(lambda text: {"text": text}) + | prompt + | llm + | StrOutputParser() +) diff --git a/examples/agent_frameworks/langchain/requirements.txt b/examples/agent_frameworks/langchain/requirements.txt new file mode 100644 index 00000000000..3e6c1e43e22 --- /dev/null +++ b/examples/agent_frameworks/langchain/requirements.txt @@ -0,0 +1,5 @@ +langchain>=0.2.0 +langchain-openai>=0.2.0 +langchain-community>=0.2.0 +beautifulsoup4 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/langgraph/langgraph_agent.py b/examples/agent_frameworks/langgraph/langgraph_agent.py new file mode 100644 index 00000000000..57b332915d7 --- /dev/null +++ b/examples/agent_frameworks/langgraph/langgraph_agent.py @@ -0,0 +1,19 @@ +from langgraph.prebuilt import create_react_agent + + +def get_weather(city: str) -> str: + """Get weather for a given city.""" + return f"It's always sunny in {city}!" + + +agent = create_react_agent( + model="openai:gpt-4o-mini", + tools=[get_weather], + prompt="You are a helpful assistant", +) + +# # Run the agent +# result = agent.invoke( +# {"messages": [{"role": "user", "content": "what is the weather in sf"}]} +# ) +# print(result) diff --git a/examples/agent_frameworks/langgraph/requirements.txt b/examples/agent_frameworks/langgraph/requirements.txt new file mode 100644 index 00000000000..3614460b75b --- /dev/null +++ b/examples/agent_frameworks/langgraph/requirements.txt @@ -0,0 +1,2 @@ +langgraph>=0.5.3 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/llama_index/agent.py b/examples/agent_frameworks/llama_index/agent.py new file mode 100644 index 00000000000..e8e8f2688d3 --- /dev/null +++ b/examples/agent_frameworks/llama_index/agent.py @@ -0,0 +1,28 @@ +import os + +from llama_index.core.agent.workflow import FunctionAgent +from llama_index.llms.openai import OpenAI + + +# Define simple tools +def get_weather(city: str) -> str: + """Get weather for a given city.""" + return f"It's always sunny in {city}!" + + +def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> str: + """Calculate tip amount for a restaurant bill.""" + tip = bill_amount * (tip_percentage / 100) + total = bill_amount + tip + return f"Tip: ${tip:.2f}, Total: ${total:.2f}" + + +# Create the agent +agent = FunctionAgent( + tools=[get_weather, calculate_tip], + llm=OpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY")), + system_prompt=( + "You are a helpful assistant that can check weather for any city " + "and calculate restaurant tips on demand." + ), +) diff --git a/examples/agent_frameworks/llama_index/requirements.txt b/examples/agent_frameworks/llama_index/requirements.txt new file mode 100644 index 00000000000..073d2d0d1f7 --- /dev/null +++ b/examples/agent_frameworks/llama_index/requirements.txt @@ -0,0 +1,3 @@ +llama-index +llama-index-llms-openai +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/openai_agents_sdk/openai_agent.py b/examples/agent_frameworks/openai_agents_sdk/openai_agent.py new file mode 100644 index 00000000000..6ffc867267e --- /dev/null +++ b/examples/agent_frameworks/openai_agents_sdk/openai_agent.py @@ -0,0 +1,20 @@ +from agents import Agent + + +def get_city_info(city: str) -> str: + """Provides a fun fact about a given city.""" + if "paris" in city.lower(): + return "Paris is known as the 'City of Light'." + elif "tokyo" in city.lower(): + return "Tokyo is the most populous metropolitan area in the world." + else: + return f"Sorry, I don't have a fun fact for {city}." + + +# PanAgent's adapter will discover this 'agent' variable by convention. +agent = Agent( + name="FactBot", + instructions="You are a helpful assistant that provides fun facts about cities using your tools.", + tools=[get_city_info], + # model="gpt-4o-mini" # The SDK defaults to a suitable model. +) diff --git a/examples/agent_frameworks/openai_agents_sdk/requirements.txt b/examples/agent_frameworks/openai_agents_sdk/requirements.txt new file mode 100644 index 00000000000..e05958deea6 --- /dev/null +++ b/examples/agent_frameworks/openai_agents_sdk/requirements.txt @@ -0,0 +1,2 @@ +openai-agents>=0.1.0 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/pydanticai/my_tool.py b/examples/agent_frameworks/pydanticai/my_tool.py new file mode 100644 index 00000000000..a8f37c32562 --- /dev/null +++ b/examples/agent_frameworks/pydanticai/my_tool.py @@ -0,0 +1,2 @@ +def get_secret_data() -> str: + return "something_secret" diff --git a/examples/agent_frameworks/pydanticai/pydanticai_agent.py b/examples/agent_frameworks/pydanticai/pydanticai_agent.py new file mode 100644 index 00000000000..77e3768d65c --- /dev/null +++ b/examples/agent_frameworks/pydanticai/pydanticai_agent.py @@ -0,0 +1,12 @@ +from my_tool import get_secret_data +from pydantic_ai import Agent + +agent = Agent( + "openai:gpt-4o-mini", + system_prompt="Be concise, reply with one sentence.", + tools=[get_secret_data], +) + +# Test code removed - agent will be invoked via FastAPI +# result = agent.run_sync("What is the secret data?") +# print(result) diff --git a/examples/agent_frameworks/pydanticai/requirements.txt b/examples/agent_frameworks/pydanticai/requirements.txt new file mode 100644 index 00000000000..4b70cd1e584 --- /dev/null +++ b/examples/agent_frameworks/pydanticai/requirements.txt @@ -0,0 +1,2 @@ +pydantic-ai>=0.4.3 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/semantic-kernel/requirements.txt b/examples/agent_frameworks/semantic-kernel/requirements.txt new file mode 100644 index 00000000000..d43590657b9 --- /dev/null +++ b/examples/agent_frameworks/semantic-kernel/requirements.txt @@ -0,0 +1,2 @@ +semantic-kernel>=1.6.2 +zenml \ No newline at end of file diff --git a/examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py b/examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py new file mode 100644 index 00000000000..cf770a5777e --- /dev/null +++ b/examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py @@ -0,0 +1,120 @@ +""" +semantic_kernel_agent.py +~~~~~~~~~~~~~~~~~~~~~~~~ + +Stage 1 Semantic Kernel example for PanAgent. + +Discovery contract: + * Global ``kernel`` variable (Semantic Kernel ``Kernel`` instance) + * Optional ``create_kernel()`` factory lets adapters rebuild kernels + with alternative services or settings. + +Safe-import rule: + No network calls or async work is executed at module import time. +""" + +from __future__ import annotations + +import asyncio +from typing import Annotated + +from semantic_kernel import Kernel +from semantic_kernel.connectors.ai.function_choice_behavior import ( + FunctionChoiceBehavior, +) +from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion +from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import ( + OpenAIChatPromptExecutionSettings, +) +from semantic_kernel.contents.chat_history import ChatHistory +from semantic_kernel.functions import kernel_function + + +# --------------------------------------------------------------------------- # +# Example native plugin # +# --------------------------------------------------------------------------- # +class WeatherPlugin: + """A very small native plugin with one callable function.""" + + @kernel_function( + name="get_weather", + description="Return a short weather report for the specified city.", + ) + def get_weather( + self, + city: Annotated[str, "The city to get weather for"], + ) -> str: + """ + Pretend to fetch weather information. + + In real scenarios you might call an external API here. + """ + return f"It's always sunny in {city}!" + + +# --------------------------------------------------------------------------- # +# Kernel factory # +# --------------------------------------------------------------------------- # +def create_kernel( + *, + model_id: str = "gpt-4o-mini", + service_id: str = "openai-chat", +) -> Kernel: + """ + Build and return a ready-to-use Semantic Kernel instance. + + Parameters + ---------- + model_id: + OpenAI model to register (default: ``gpt-4o-mini``). + service_id: + Service identifier used when registering the chat completion service. + """ + kernel = Kernel() + + # Register OpenAI chat completion service (no network call at import time) + kernel.add_service( + OpenAIChatCompletion(ai_model_id=model_id, service_id=service_id) + ) + + # Register our Weather plugin + kernel.add_plugin(WeatherPlugin(), plugin_name="Weather") + + return kernel + + +# --------------------------------------------------------------------------- # +# PanAgent discovery variable # +# --------------------------------------------------------------------------- # +# IMPORTANT: PanAgent looks for this global name when framework == "semantic-kernel" +kernel: Kernel = create_kernel() + +__all__ = ["kernel", "create_kernel", "WeatherPlugin"] + + +# --------------------------------------------------------------------------- # +# Interactive demo (run only when executed directly) # +# --------------------------------------------------------------------------- # +async def _demo() -> None: # pragma: no cover – illustrative only + """Simple interactive test of function-calling behaviour.""" + chat_service = kernel.get_service(OpenAIChatCompletion) # type: ignore[arg-type] + + # Chat history with a user request that triggers function calling + history = ChatHistory() + history.add_user_message("What is the weather in Paris?") + + # Enable automatic function choice so the model may call our plugin + settings = OpenAIChatPromptExecutionSettings() + settings.function_choice_behavior = FunctionChoiceBehavior.Auto() + + response = await chat_service.get_chat_message_content( # type: ignore[attr-defined] + chat_history=history, + settings=settings, + kernel=kernel, + ) + print(f"Assistant: {response.content}") # noqa: T201 – demo print + + +if __name__ == "__main__": # pragma: no cover + # Run the async demo when the script is executed directly + asyncio.run(_demo()) From 47f4f3b5b940aa72d1a90ae7b81c7d95bc243893 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sat, 23 Aug 2025 19:11:31 +0200 Subject: [PATCH 2/8] Rename --- .../agent_framework_integrations/README.md | 219 ++++++++++++++++++ .../autogen/README.md | 16 ++ .../autogen/requirements.txt | 0 .../autogen/run.py} | 60 ++--- .../aws-strands/README.md | 16 ++ .../aws-strands/requirements.txt | 0 .../aws-strands/run.py} | 20 +- .../crewai/README.md | 16 ++ .../crewai/requirements.txt | 0 .../crewai/run.py} | 40 ++-- .../google-adk/README.md | 16 ++ .../google-adk/requirements.txt | 0 .../google-adk/run.py} | 44 ++-- .../haystack/README.md | 16 ++ .../haystack/requirements.txt | 0 .../haystack/run.py | 105 +++++++++ .../langchain/README.md | 16 ++ .../langchain/requirements.txt | 0 .../langchain/run.py | 94 ++++++++ .../langgraph/README.md | 16 ++ .../langgraph/requirements.txt | 4 + .../langgraph/run.py | 100 ++++++++ .../llama_index/README.md | 16 ++ .../llama_index/requirements.txt | 0 .../llama_index/run.py | 106 +++++++++ .../openai_agents_sdk/README.md | 16 ++ .../openai_agents_sdk/requirements.txt | 0 .../openai_agents_sdk/run.py | 90 +++++++ .../pydanticai/README.md | 16 ++ .../pydanticai/requirements.txt | 0 .../pydanticai/run.py | 86 +++++++ .../semantic-kernel/README.md | 16 ++ .../semantic-kernel/requirements.txt | 0 .../semantic-kernel/run.py | 122 ++++++++++ examples/agent_frameworks/README.md | 143 ------------ .../agent_frameworks/autogen/autogen_agent.py | 202 ---------------- .../agent_frameworks/aws-strands/agent.py | 42 ---- .../agent_frameworks/crewai/crewai_agent.py | 54 ----- .../agent_frameworks/google-adk/adk_agent.py | 43 ---- .../haystack/haystack_agent.py | 88 ------- .../langchain/langchain_agent.py | 36 --- .../langgraph/langgraph_agent.py | 19 -- .../langgraph/requirements.txt | 2 - .../agent_frameworks/llama_index/agent.py | 28 --- .../openai_agents_sdk/openai_agent.py | 20 -- .../agent_frameworks/pydanticai/my_tool.py | 2 - .../pydanticai/pydanticai_agent.py | 12 - .../semantic-kernel/semantic_kernel_agent.py | 120 ---------- 48 files changed, 1188 insertions(+), 889 deletions(-) create mode 100644 examples/agent_framework_integrations/README.md create mode 100644 examples/agent_framework_integrations/autogen/README.md rename examples/{agent_frameworks => agent_framework_integrations}/autogen/requirements.txt (100%) rename examples/{agent_frameworks/autogen/pipeline.py => agent_framework_integrations/autogen/run.py} (81%) create mode 100644 examples/agent_framework_integrations/aws-strands/README.md rename examples/{agent_frameworks => agent_framework_integrations}/aws-strands/requirements.txt (100%) rename examples/{agent_frameworks/aws-strands/pipeline.py => agent_framework_integrations/aws-strands/run.py} (92%) create mode 100644 examples/agent_framework_integrations/crewai/README.md rename examples/{agent_frameworks => agent_framework_integrations}/crewai/requirements.txt (100%) rename examples/{agent_frameworks/crewai/pipeline.py => agent_framework_integrations/crewai/run.py} (84%) create mode 100644 examples/agent_framework_integrations/google-adk/README.md rename examples/{agent_frameworks => agent_framework_integrations}/google-adk/requirements.txt (100%) rename examples/{agent_frameworks/google-adk/pipeline.py => agent_framework_integrations/google-adk/run.py} (81%) create mode 100644 examples/agent_framework_integrations/haystack/README.md rename examples/{agent_frameworks => agent_framework_integrations}/haystack/requirements.txt (100%) create mode 100644 examples/agent_framework_integrations/haystack/run.py create mode 100644 examples/agent_framework_integrations/langchain/README.md rename examples/{agent_frameworks => agent_framework_integrations}/langchain/requirements.txt (100%) create mode 100644 examples/agent_framework_integrations/langchain/run.py create mode 100644 examples/agent_framework_integrations/langgraph/README.md create mode 100644 examples/agent_framework_integrations/langgraph/requirements.txt create mode 100644 examples/agent_framework_integrations/langgraph/run.py create mode 100644 examples/agent_framework_integrations/llama_index/README.md rename examples/{agent_frameworks => agent_framework_integrations}/llama_index/requirements.txt (100%) create mode 100644 examples/agent_framework_integrations/llama_index/run.py create mode 100644 examples/agent_framework_integrations/openai_agents_sdk/README.md rename examples/{agent_frameworks => agent_framework_integrations}/openai_agents_sdk/requirements.txt (100%) create mode 100644 examples/agent_framework_integrations/openai_agents_sdk/run.py create mode 100644 examples/agent_framework_integrations/pydanticai/README.md rename examples/{agent_frameworks => agent_framework_integrations}/pydanticai/requirements.txt (100%) create mode 100644 examples/agent_framework_integrations/pydanticai/run.py create mode 100644 examples/agent_framework_integrations/semantic-kernel/README.md rename examples/{agent_frameworks => agent_framework_integrations}/semantic-kernel/requirements.txt (100%) create mode 100644 examples/agent_framework_integrations/semantic-kernel/run.py delete mode 100644 examples/agent_frameworks/README.md delete mode 100644 examples/agent_frameworks/autogen/autogen_agent.py delete mode 100644 examples/agent_frameworks/aws-strands/agent.py delete mode 100644 examples/agent_frameworks/crewai/crewai_agent.py delete mode 100644 examples/agent_frameworks/google-adk/adk_agent.py delete mode 100644 examples/agent_frameworks/haystack/haystack_agent.py delete mode 100644 examples/agent_frameworks/langchain/langchain_agent.py delete mode 100644 examples/agent_frameworks/langgraph/langgraph_agent.py delete mode 100644 examples/agent_frameworks/langgraph/requirements.txt delete mode 100644 examples/agent_frameworks/llama_index/agent.py delete mode 100644 examples/agent_frameworks/openai_agents_sdk/openai_agent.py delete mode 100644 examples/agent_frameworks/pydanticai/my_tool.py delete mode 100644 examples/agent_frameworks/pydanticai/pydanticai_agent.py delete mode 100644 examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py diff --git a/examples/agent_framework_integrations/README.md b/examples/agent_framework_integrations/README.md new file mode 100644 index 00000000000..d21515f2557 --- /dev/null +++ b/examples/agent_framework_integrations/README.md @@ -0,0 +1,219 @@ +
+
+ + ZenML Logo + + +

Agent Frameworks Integration Examples

+ +

+ Production-ready agent orchestration with ZenML +
+ Features + Β· + Roadmap + Β· + Report Bug + Β· + Vote New Features + Β· + Read Blog +
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+ + Slack + + + LinkedIn + + + Twitter + +

+
+ +# πŸ€– Agent Frameworks + ZenML + +This collection demonstrates how to integrate popular agent frameworks with ZenML for production-grade AI agent orchestration. Each example follows consistent patterns and best practices, making it easy to adapt any framework for your specific use case. + +## πŸš€ Quick Start + +Choose any framework and get started in minutes: + +```bash +cd framework-name/ +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## πŸ“Š Frameworks Overview + +| Framework | Type | Key Features | Technologies | +|-----------|------|-------------|-------------| +| [Autogen](autogen/) | 🀝 Multi-Agent | Multi-agent conversations, Role-based collaboration | autogen, openai | +| [AWS Strands](aws-strands/) | ⚑ Simple | Direct agent calls, Built-in tools | aws-agents, bedrock | +| [CrewAI](crewai/) | πŸ‘₯ Crews | Agent crews, Task delegation | crewai, openai | +| [Google ADK](google-adk/) | 🧠 Gemini | Google AI agents, Gemini models | google-adk, gemini | +| [Haystack](haystack/) | πŸ” RAG | Retrieval pipelines, Document processing | haystack, openai | +| [LangChain](langchain/) | πŸ”— Chains | Runnable chains, Tool composition | langchain, openai | +| [LangGraph](langgraph/) | πŸ•ΈοΈ Graphs | ReAct agents, Graph workflows | langgraph, openai | +| [LlamaIndex](llama_index/) | πŸ“š Functions | Function agents, Async execution | llama-index, openai | +| [OpenAI Agents SDK](openai_agents_sdk/) | πŸ—οΈ Structured | Official OpenAI agents, Structured execution | openai-agents, openai | +| [PydanticAI](pydanticai/) | βœ… Type-Safe | Type-safe agents, Validation | pydantic-ai, openai | +| [Semantic Kernel](semantic-kernel/) | 🧩 Plugins | Plugin architecture, Microsoft ecosystem | semantic-kernel, openai | + +## 🎯 Core Patterns + +All examples follow these established patterns: + +### πŸ”§ Environment Setup +- **uv for environments**: `uv venv --python 3.11` +- **Fast installs**: `uv pip install -r requirements.txt` +- **Consistent Python version**: 3.11 across all frameworks + +### πŸ“¦ Pipeline Architecture +```python +@pipeline +def agent_pipeline() -> str: + # 1. External artifact input + query = ExternalArtifact(value="Your query") + + # 2. Agent execution + results = run_agent(query) + + # 3. Response formatting + summary = format_response(results) + + return summary +``` + +### πŸ›‘οΈ Error Handling +- Comprehensive try-catch blocks +- Status tracking with success/error states +- Graceful degradation for agent failures + +### πŸ“Š Artifact Management +- **Annotated outputs**: `Annotated[Type, "artifact_name"]` +- **ZenML integration**: Full pipeline orchestration +- **Artifact storage**: S3-backed artifact management + +## πŸ—οΈ Framework-Specific Features + +### Multi-Agent Systems +- **Autogen**: Conversational multi-agent workflows +- **CrewAI**: Role-based agent crews with task delegation + +### Simple Execution +- **AWS Strands**: Direct callable interface with `agent(query)` +- **PydanticAI**: Clean `agent.run_sync(query)` API +- **Google ADK**: Gemini-powered agents with simple calls + +### Advanced Orchestration +- **LangChain**: Composable chains with tool integration +- **LangGraph**: ReAct pattern with graph-based workflows +- **Semantic Kernel**: Plugin-based architecture + +### Specialized Use Cases +- **Haystack**: RAG pipelines with retrieval components +- **LlamaIndex**: Function agents with async capabilities +- **OpenAI Agents SDK**: Structured execution with OpenAI + +## πŸ’‘ Key Learnings + +1. **ExternalArtifact simplifies inputs**: No need for input steps +2. **Framework APIs vary significantly**: Each requires specific execution patterns +3. **Async handling matters**: Some frameworks require event loop management +4. **Error handling is crucial**: Agent execution can fail in many ways +5. **Consistent patterns scale**: Following established patterns makes integration easier + +## πŸ”„ Implementation Notes + +### Async Frameworks +Some frameworks require async handling within ZenML steps: +- **LlamaIndex**: `asyncio.run(agent.run(query))` +- **Semantic Kernel**: Event loop management for chat completion + +### Tool Integration +Different frameworks have varying tool patterns: +- **Decorators**: `@tool`, `@function_tool`, `@kernel_function` +- **Functions**: Regular Python functions as tools +- **Classes**: Tool classes with specific interfaces + +### Response Extraction +Each framework returns different response types: +- **String responses**: Direct text output +- **Object responses**: `.output`, `.content`, `.final_output` attributes +- **Complex responses**: Nested structures requiring extraction + +## πŸ“‹ Requirements + +- **Python**: 3.11+ +- **ZenML**: Latest version +- **UV**: For fast package management +- **OpenAI API Key**: Most examples use OpenAI (set `OPENAI_API_KEY`) + +## πŸ› οΈ Development + +### Adding New Frameworks + +1. Create folder structure: + ``` + new-framework/ + β”œβ”€β”€ README.md + β”œβ”€β”€ requirements.txt + └── run.py + ``` + +2. Follow the three-step pipeline pattern +3. Add framework-specific notes to main README +4. Test with ZenML integration + +### Code Quality +- **Error handling**: Always wrap agent execution in try-catch +- **Resource management**: Properly start/stop runtimes +- **Artifact naming**: Use descriptive, consistent names +- **Documentation**: Clear, concise README per framework + +## πŸ†˜ Getting Help + +- πŸ’¬ [Join our Slack community](https://zenml.io/slack) +- πŸ“– [Check our documentation](https://docs.zenml.io/) +- πŸ› [Report issues](https://github.com/zenml-io/zenml/issues) +- πŸ’‘ [Request features](https://zenml.io/discussion) + +## 🌟 About ZenML + +ZenML is an extensible, open-source MLOps framework for creating production-ready ML pipelines. These agent framework integrations showcase ZenML's flexibility in orchestrating AI workflows beyond traditional ML use cases. + +**Why ZenML for Agent Orchestration?** +- πŸ”„ **Reproducible workflows**: Version and track agent executions +- πŸ“Š **Artifact management**: Store and version agent inputs/outputs +- 🎯 **Production ready**: Built-in monitoring, logging, and error handling +- πŸ”§ **Tool agnostic**: Works with any agent framework +- ☁️ **Cloud native**: Deploy anywhere with consistent behavior + +## πŸ“– Learn More + +| Resource | Description | +|----------|-------------| +| 🧘 **[ZenML 101]** | New to ZenML? Start here! | +| βš› **[Core Concepts]** | Understand ZenML fundamentals | +| πŸ€– **[LLMOps Guide]** | Complete guide to LLMOps with ZenML | +| πŸ““ **[Documentation]** | Full ZenML documentation | +| πŸ“’ **[API Reference]** | Detailed API documentation | +| ⚽ **[Examples]** | More ZenML examples | +| πŸ”ˆ **[Podcast]** | ML conversations every 2 weeks | + +[ZenML 101]: https://docs.zenml.io/user-guides/starter-guide +[Core Concepts]: https://docs.zenml.io/getting-started/core-concepts +[LLMOps Guide]: https://docs.zenml.io/user-guides/llmops-guide +[Documentation]: https://docs.zenml.io/ +[API Reference]: https://apidocs.zenml.io/ +[Examples]: https://github.com/zenml-io/zenml/tree/main/examples +[Podcast]: https://podcast.zenml.io/ + +--- + +*This collection demonstrates the power and flexibility of ZenML for orchestrating diverse agent frameworks in production environments.* \ No newline at end of file diff --git a/examples/agent_framework_integrations/autogen/README.md b/examples/agent_framework_integrations/autogen/README.md new file mode 100644 index 00000000000..18dd09d83e1 --- /dev/null +++ b/examples/agent_framework_integrations/autogen/README.md @@ -0,0 +1,16 @@ +# Autogen + ZenML + +Multi-agent conversation framework integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Multi-agent conversations with UserProxy and Assistant agents +- Async runtime management with proper cleanup +- Travel planning use case with collaborative agents \ No newline at end of file diff --git a/examples/agent_frameworks/autogen/requirements.txt b/examples/agent_framework_integrations/autogen/requirements.txt similarity index 100% rename from examples/agent_frameworks/autogen/requirements.txt rename to examples/agent_framework_integrations/autogen/requirements.txt diff --git a/examples/agent_frameworks/autogen/pipeline.py b/examples/agent_framework_integrations/autogen/run.py similarity index 81% rename from examples/agent_frameworks/autogen/pipeline.py rename to examples/agent_framework_integrations/autogen/run.py index af7616b7096..7ff587866bd 100644 --- a/examples/agent_frameworks/autogen/pipeline.py +++ b/examples/agent_framework_integrations/autogen/run.py @@ -8,48 +8,48 @@ import asyncio from typing import Annotated, Any, Dict +from autogen_agent import TravelQuery, setup_runtime from autogen_core import AgentId -from zenml import ExternalArtifact, pipeline, step -from autogen_agent import TravelQuery, setup_runtime +from zenml import ExternalArtifact, pipeline, step @step -def run_autogen_agents(query: str) -> Annotated[Dict[str, Any], "agent_results"]: +def run_autogen_agents( + query: str, +) -> Annotated[Dict[str, Any], "agent_results"]: """Execute the autogen multi-agent system and return results.""" - # Parse destination and days from query string # Format: "Plan a X-day trip to DESTINATION" parts = query.split(" to ") destination = parts[1] if len(parts) > 1 else "Unknown" days_part = [w for w in query.split() if w.endswith("-day")][0] days = int(days_part.replace("-day", "")) - + # Create TravelQuery for the agents travel_query = TravelQuery(destination=destination, days=days) - + async def _run_agents(): # Setup the autogen runtime runtime = await setup_runtime() - + # Start the runtime runtime.start() - + try: # Send the query to the coordinator agent result = await runtime.send_message( - travel_query, - AgentId("coordinator", "default") + travel_query, AgentId("coordinator", "default") ) return result - + finally: # Always stop the runtime await runtime.stop() - + # Run the async function travel_plan = asyncio.run(_run_agents()) - + # Convert to dict for ZenML artifact storage return { "destination": travel_plan.destination, @@ -62,50 +62,52 @@ async def _run_agents(): "attractions": { "destination": travel_plan.attractions.destination, "attractions": travel_plan.attractions.attractions, - } + }, } @step -def format_travel_plan(plan_data: Dict[str, Any]) -> Annotated[str, "formatted_plan"]: +def format_travel_plan( + plan_data: Dict[str, Any], +) -> Annotated[str, "formatted_plan"]: """Format the travel plan into a readable summary.""" weather = plan_data["weather"] attractions = plan_data["attractions"] - - summary = f"""πŸ–οΈ TRAVEL PLAN FOR {plan_data['destination'].upper()} -{'=' * 50} -πŸ“… Duration: {plan_data['days']} days + summary = f"""πŸ–οΈ TRAVEL PLAN FOR {plan_data["destination"].upper()} +{"=" * 50} + +πŸ“… Duration: {plan_data["days"]} days 🌀️ Weather Forecast: - β€’ Condition: {weather['forecast']} - β€’ Temperature: {weather['temperature']} + β€’ Condition: {weather["forecast"]} + β€’ Temperature: {weather["temperature"]} πŸ›οΈ Top Attractions: """ - - for i, attraction in enumerate(attractions['attractions'], 1): + + for i, attraction in enumerate(attractions["attractions"], 1): summary += f" {i}. {attraction}\n" - + return summary.strip() @pipeline def autogen_travel_pipeline() -> str: """ZenML pipeline that orchestrates the autogen multi-agent travel system. - + Returns: Formatted travel plan summary """ # External artifact for travel query travel_query = ExternalArtifact(value="Plan a 4-day trip to Paris") - + # Run the autogen agents plan_data = run_autogen_agents(travel_query) - + # Format the results summary = format_travel_plan(plan_data) - + return summary @@ -113,4 +115,4 @@ def autogen_travel_pipeline() -> str: print("πŸš€ Running autogen travel pipeline...") run_result = autogen_travel_pipeline() print("Pipeline completed successfully!") - print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/aws-strands/README.md b/examples/agent_framework_integrations/aws-strands/README.md new file mode 100644 index 00000000000..d3a2f30bc1e --- /dev/null +++ b/examples/agent_framework_integrations/aws-strands/README.md @@ -0,0 +1,16 @@ +# AWS Strands + ZenML + +AWS Strands agent framework integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Simple agent execution with callable interface +- Built-in tools using `@tool` decorator +- Math calculation capabilities \ No newline at end of file diff --git a/examples/agent_frameworks/aws-strands/requirements.txt b/examples/agent_framework_integrations/aws-strands/requirements.txt similarity index 100% rename from examples/agent_frameworks/aws-strands/requirements.txt rename to examples/agent_framework_integrations/aws-strands/requirements.txt diff --git a/examples/agent_frameworks/aws-strands/pipeline.py b/examples/agent_framework_integrations/aws-strands/run.py similarity index 92% rename from examples/agent_frameworks/aws-strands/pipeline.py rename to examples/agent_framework_integrations/aws-strands/run.py index 5b2ccffcea5..c1610d99059 100644 --- a/examples/agent_frameworks/aws-strands/pipeline.py +++ b/examples/agent_framework_integrations/aws-strands/run.py @@ -7,10 +7,10 @@ from typing import Annotated -from zenml import ExternalArtifact, pipeline, step - from agent import agent +from zenml import ExternalArtifact, pipeline, step + @step def run_strands_agent(query: str) -> Annotated[str, "agent_response"]: @@ -23,10 +23,12 @@ def run_strands_agent(query: str) -> Annotated[str, "agent_response"]: @step -def format_weather_response(response: str) -> Annotated[str, "formatted_response"]: +def format_weather_response( + response: str, +) -> Annotated[str, "formatted_response"]: """Format the agent response into a readable summary.""" formatted = f"""🌀️ WEATHER REPORT -{'=' * 30} +{"=" * 30} {response} @@ -38,19 +40,19 @@ def format_weather_response(response: str) -> Annotated[str, "formatted_response @pipeline def strands_weather_pipeline() -> str: """ZenML pipeline that orchestrates the AWS Strands weather agent. - + Returns: Formatted weather response """ # External artifact for weather query weather_query = ExternalArtifact(value="What's the weather like in Tokyo?") - + # Run the agent response = run_strands_agent(weather_query) - + # Format the results summary = format_weather_response(response) - + return summary @@ -58,4 +60,4 @@ def strands_weather_pipeline() -> str: print("πŸš€ Running AWS Strands weather pipeline...") run_result = strands_weather_pipeline() print("Pipeline completed successfully!") - print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/crewai/README.md b/examples/agent_framework_integrations/crewai/README.md new file mode 100644 index 00000000000..fb4b8cbd1ca --- /dev/null +++ b/examples/agent_framework_integrations/crewai/README.md @@ -0,0 +1,16 @@ +# CrewAI + ZenML + +CrewAI multi-agent crew framework integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Multi-agent crew with defined roles and goals +- Task delegation and collaborative execution +- Article research and writing crew example \ No newline at end of file diff --git a/examples/agent_frameworks/crewai/requirements.txt b/examples/agent_framework_integrations/crewai/requirements.txt similarity index 100% rename from examples/agent_frameworks/crewai/requirements.txt rename to examples/agent_framework_integrations/crewai/requirements.txt diff --git a/examples/agent_frameworks/crewai/pipeline.py b/examples/agent_framework_integrations/crewai/run.py similarity index 84% rename from examples/agent_frameworks/crewai/pipeline.py rename to examples/agent_framework_integrations/crewai/run.py index b7f07aebf7a..cc02dcc4d35 100644 --- a/examples/agent_frameworks/crewai/pipeline.py +++ b/examples/agent_framework_integrations/crewai/run.py @@ -5,78 +5,76 @@ and artifact management. """ -from typing import Annotated, Dict, Any - -from zenml import ExternalArtifact, pipeline, step +from typing import Annotated, Any, Dict from crewai_agent import crew +from zenml import ExternalArtifact, pipeline, step + @step def run_crewai_agents(query: str) -> Annotated[Dict[str, Any], "crew_results"]: """Execute the CrewAI crew and return results.""" # Extract city from query string city = query.split(" for ")[-1] if " for " in query else "Unknown" - + try: # Execute the crew with the city parameter result = crew.kickoff(inputs={"city": city}) - + # Convert result to dict for ZenML artifact storage - return { - "city": city, - "result": str(result), - "status": "success" - } + return {"city": city, "result": str(result), "status": "success"} except Exception as e: return { "city": city, "result": f"Crew execution error: {str(e)}", - "status": "error" + "status": "error", } @step -def format_travel_results(crew_data: Dict[str, Any]) -> Annotated[str, "formatted_results"]: +def format_travel_results( + crew_data: Dict[str, Any], +) -> Annotated[str, "formatted_results"]: """Format the CrewAI results into a readable summary.""" city = crew_data["city"] result = crew_data["result"] status = crew_data["status"] - + if status == "error": formatted = f"""❌ TRAVEL PLANNING ERROR FOR {city.upper()} -{'=' * 50} +{"=" * 50} {result} """ else: formatted = f"""✈️ TRAVEL PLANNING FOR {city.upper()} -{'=' * 50} +{"=" * 50} {result} πŸ€– Generated by CrewAI Multi-Agent System """ - + return formatted.strip() @pipeline def crewai_travel_pipeline() -> str: """ZenML pipeline that orchestrates the CrewAI travel planning system. - + Returns: Formatted travel planning results """ # External artifact for travel query travel_query = ExternalArtifact(value="Travel planning for Berlin") - + # Run the CrewAI agents crew_results = run_crewai_agents(travel_query) - + # Format the results summary = format_travel_results(crew_results) - + return summary @@ -84,4 +82,4 @@ def crewai_travel_pipeline() -> str: print("πŸš€ Running CrewAI travel pipeline...") run_result = crewai_travel_pipeline() print("Pipeline completed successfully!") - print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/google-adk/README.md b/examples/agent_framework_integrations/google-adk/README.md new file mode 100644 index 00000000000..6380e07778a --- /dev/null +++ b/examples/agent_framework_integrations/google-adk/README.md @@ -0,0 +1,16 @@ +# Google ADK + ZenML + +Google Agent Development Kit integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Google Gemini-powered agents +- Simple callable interface +- Built-in tool integration for calculations \ No newline at end of file diff --git a/examples/agent_frameworks/google-adk/requirements.txt b/examples/agent_framework_integrations/google-adk/requirements.txt similarity index 100% rename from examples/agent_frameworks/google-adk/requirements.txt rename to examples/agent_framework_integrations/google-adk/requirements.txt diff --git a/examples/agent_frameworks/google-adk/pipeline.py b/examples/agent_framework_integrations/google-adk/run.py similarity index 81% rename from examples/agent_frameworks/google-adk/pipeline.py rename to examples/agent_framework_integrations/google-adk/run.py index 6d551878ad4..3433341d376 100644 --- a/examples/agent_frameworks/google-adk/pipeline.py +++ b/examples/agent_framework_integrations/google-adk/run.py @@ -6,10 +6,10 @@ from typing import Annotated, Any, Dict -from zenml import ExternalArtifact, pipeline, step - from adk_agent import root_agent +from zenml import ExternalArtifact, pipeline, step + @step def run_adk_agent(query: str) -> Annotated[Dict[str, Any], "agent_results"]: @@ -17,47 +17,53 @@ def run_adk_agent(query: str) -> Annotated[Dict[str, Any], "agent_results"]: try: # Check what methods the agent has agent_response = None - if hasattr(root_agent, '__call__'): + if hasattr(root_agent, "__call__"): agent_response = root_agent(query) - elif hasattr(root_agent, 'run'): + elif hasattr(root_agent, "run"): agent_response = root_agent.run(query) - elif hasattr(root_agent, 'execute'): + elif hasattr(root_agent, "execute"): agent_response = root_agent.execute(query) else: # Fallback - just return info about available methods - methods = [method for method in dir(root_agent) if not method.startswith('_')] + methods = [ + method + for method in dir(root_agent) + if not method.startswith("_") + ] agent_response = f"Available methods: {methods}" - + return { "query": query, "result": str(agent_response), - "status": "success" + "status": "success", } except Exception as e: return { "query": query, "result": f"Agent error: {str(e)}", - "status": "error" + "status": "error", } @step -def format_adk_response(agent_data: Dict[str, Any]) -> Annotated[str, "formatted_response"]: +def format_adk_response( + agent_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: """Format the Google ADK agent results into a readable summary.""" query = agent_data["query"] result = agent_data["result"] status = agent_data["status"] - + if status == "error": formatted = f"""❌ GOOGLE ADK AGENT ERROR -{'=' * 40} +{"=" * 40} Query: {query} Error: {result} """ else: formatted = f"""πŸ€– GOOGLE ADK AGENT RESPONSE -{'=' * 40} +{"=" * 40} Query: {query} @@ -66,26 +72,26 @@ def format_adk_response(agent_data: Dict[str, Any]) -> Annotated[str, "formatted πŸ”§ Powered by Google ADK """ - + return formatted.strip() @pipeline def google_adk_pipeline() -> str: """ZenML pipeline that orchestrates the Google ADK agent system. - + Returns: Formatted agent response """ # External artifact for agent query agent_query = ExternalArtifact(value="What's the weather like in Tokyo?") - + # Run the Google ADK agent agent_results = run_adk_agent(agent_query) - + # Format the results summary = format_adk_response(agent_results) - + return summary @@ -93,4 +99,4 @@ def google_adk_pipeline() -> str: print("πŸš€ Running Google ADK agent pipeline...") run_result = google_adk_pipeline() print("Pipeline completed successfully!") - print("Check the ZenML dashboard for detailed results and artifacts.") \ No newline at end of file + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/haystack/README.md b/examples/agent_framework_integrations/haystack/README.md new file mode 100644 index 00000000000..dee77820ec3 --- /dev/null +++ b/examples/agent_framework_integrations/haystack/README.md @@ -0,0 +1,16 @@ +# Haystack + ZenML + +Haystack RAG pipeline integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Retrieval-Augmented Generation (RAG) pipeline +- Component-based architecture +- In-memory document store with retrieval capabilities \ No newline at end of file diff --git a/examples/agent_frameworks/haystack/requirements.txt b/examples/agent_framework_integrations/haystack/requirements.txt similarity index 100% rename from examples/agent_frameworks/haystack/requirements.txt rename to examples/agent_framework_integrations/haystack/requirements.txt diff --git a/examples/agent_framework_integrations/haystack/run.py b/examples/agent_framework_integrations/haystack/run.py new file mode 100644 index 00000000000..5df58e35090 --- /dev/null +++ b/examples/agent_framework_integrations/haystack/run.py @@ -0,0 +1,105 @@ +"""ZenML Pipeline for Haystack RAG System. + +This pipeline demonstrates how to integrate Haystack RAG pipelines with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from haystack_agent import pipeline as haystack_pipeline + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_haystack_rag( + question: str, +) -> Annotated[Dict[str, Any], "rag_results"]: + """Execute the Haystack RAG pipeline and return results.""" + try: + result = haystack_pipeline.run( + { + "retriever": {"query": question}, + "prompt_builder": {"question": question}, + }, + include_outputs_from={"llm"}, + ) + + # Extract the response from the nested structure + if ( + "llm" in result + and "replies" in result["llm"] + and result["llm"]["replies"] + ): + response = result["llm"]["replies"][ + 0 + ] # Already a string in newer Haystack versions + else: + response = "No response generated" + + return {"question": question, "answer": response, "status": "success"} + except Exception as e: + return { + "question": question, + "answer": f"RAG error: {str(e)}", + "status": "error", + } + + +@step +def format_rag_response( + rag_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the Haystack RAG results into a readable summary.""" + question = rag_data["question"] + answer = rag_data["answer"] + status = rag_data["status"] + + if status == "error": + formatted = f"""❌ HAYSTACK RAG ERROR +{"=" * 40} + +Question: {question} +Error: {answer} +""" + else: + formatted = f"""πŸ” HAYSTACK RAG RESPONSE +{"=" * 40} + +Question: {question} + +Answer: +{answer} + +πŸ€– Powered by Haystack RAG (BM25 + OpenAI) +""" + + return formatted.strip() + + +@pipeline +def haystack_rag_pipeline() -> str: + """ZenML pipeline that orchestrates the Haystack RAG system. + + Returns: + Formatted RAG response + """ + # External artifact for RAG query + rag_query = ExternalArtifact( + value="What city is home to the Eiffel Tower?" + ) + + # Run the Haystack RAG pipeline + rag_results = run_haystack_rag(rag_query) + + # Format the results + summary = format_rag_response(rag_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running Haystack RAG pipeline...") + run_result = haystack_rag_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/langchain/README.md b/examples/agent_framework_integrations/langchain/README.md new file mode 100644 index 00000000000..356faf9c2c9 --- /dev/null +++ b/examples/agent_framework_integrations/langchain/README.md @@ -0,0 +1,16 @@ +# LangChain + ZenML + +LangChain agent chain integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Runnable chains with tool integration +- Math and web search capabilities +- Composable chain architecture with pipe operators \ No newline at end of file diff --git a/examples/agent_frameworks/langchain/requirements.txt b/examples/agent_framework_integrations/langchain/requirements.txt similarity index 100% rename from examples/agent_frameworks/langchain/requirements.txt rename to examples/agent_framework_integrations/langchain/requirements.txt diff --git a/examples/agent_framework_integrations/langchain/run.py b/examples/agent_framework_integrations/langchain/run.py new file mode 100644 index 00000000000..b96b40c51c4 --- /dev/null +++ b/examples/agent_framework_integrations/langchain/run.py @@ -0,0 +1,94 @@ +"""ZenML Pipeline for LangChain Document Summarization. + +This pipeline demonstrates how to integrate LangChain chains with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from langchain_agent import chain + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_langchain_chain( + url_input: str, +) -> Annotated[Dict[str, Any], "chain_results"]: + """Execute the LangChain chain for document summarization.""" + try: + # Extract URL from input (assuming format "Summarize: URL") + if ":" in url_input and url_input.startswith("Summarize"): + url = url_input.split(":", 1)[1].strip() + else: + url = url_input # Fallback to use input as URL directly + + # Execute the LangChain chain + result = chain.invoke({"url": url}) + + return {"url": url, "summary": result, "status": "success"} + except Exception as e: + return { + "url": url_input, + "summary": f"Chain error: {str(e)}", + "status": "error", + } + + +@step +def format_langchain_response( + chain_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the LangChain results into a readable summary.""" + url = chain_data["url"] + summary = chain_data["summary"] + status = chain_data["status"] + + if status == "error": + formatted = f"""❌ LANGCHAIN CHAIN ERROR +{"=" * 40} + +URL: {url} +Error: {summary} +""" + else: + formatted = f"""πŸ“„ LANGCHAIN DOCUMENT SUMMARY +{"=" * 40} + +Source: {url} + +Summary: +{summary} + +🦜 Powered by LangChain (WebLoader + OpenAI) +""" + + return formatted.strip() + + +@pipeline +def langchain_summarization_pipeline() -> str: + """ZenML pipeline that orchestrates the LangChain document summarization. + + Returns: + Formatted document summary + """ + # External artifact for URL to summarize + document_url = ExternalArtifact( + value="Summarize: https://python.langchain.com/docs/introduction/" + ) + + # Run the LangChain chain + chain_results = run_langchain_chain(document_url) + + # Format the results + summary = format_langchain_response(chain_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running LangChain summarization pipeline...") + run_result = langchain_summarization_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/langgraph/README.md b/examples/agent_framework_integrations/langgraph/README.md new file mode 100644 index 00000000000..2dcf4e5bd1c --- /dev/null +++ b/examples/agent_framework_integrations/langgraph/README.md @@ -0,0 +1,16 @@ +# LangGraph + ZenML + +LangGraph ReAct agent integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- ReAct (Reasoning + Acting) agent pattern +- Message-based agent communication +- Built-in search and calculation tools \ No newline at end of file diff --git a/examples/agent_framework_integrations/langgraph/requirements.txt b/examples/agent_framework_integrations/langgraph/requirements.txt new file mode 100644 index 00000000000..dc45de22497 --- /dev/null +++ b/examples/agent_framework_integrations/langgraph/requirements.txt @@ -0,0 +1,4 @@ +langgraph>=0.5.3 +langchain>=0.2.0 +langchain-openai>=0.2.0 +zenml \ No newline at end of file diff --git a/examples/agent_framework_integrations/langgraph/run.py b/examples/agent_framework_integrations/langgraph/run.py new file mode 100644 index 00000000000..fdbaf166aa7 --- /dev/null +++ b/examples/agent_framework_integrations/langgraph/run.py @@ -0,0 +1,100 @@ +"""ZenML Pipeline for LangGraph ReAct Agent. + +This pipeline demonstrates how to integrate LangGraph agents with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from langgraph_agent import agent + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_langgraph_agent( + query: str, +) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the LangGraph ReAct agent and return results.""" + try: + # LangGraph agents expect messages in a specific format + messages = [{"role": "user", "content": query}] + result = agent.invoke({"messages": messages}) + + # Extract the response from the result + if "messages" in result and result["messages"]: + # Get the last message (assistant's response) + last_message = result["messages"][-1] + if hasattr(last_message, "content"): + response = last_message.content + else: + response = str(last_message) + else: + response = str(result) + + return {"query": query, "response": response, "status": "success"} + except Exception as e: + return { + "query": query, + "response": f"Agent error: {str(e)}", + "status": "error", + } + + +@step +def format_langgraph_response( + agent_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the LangGraph agent results into a readable summary.""" + query = agent_data["query"] + response = agent_data["response"] + status = agent_data["status"] + + if status == "error": + formatted = f"""❌ LANGGRAPH AGENT ERROR +{"=" * 40} + +Query: {query} +Error: {response} +""" + else: + formatted = f"""πŸ€– LANGGRAPH REACT AGENT RESPONSE +{"=" * 40} + +Query: {query} + +Response: +{response} + +πŸ”§ Powered by LangGraph (ReAct Agent + Tools) +""" + + return formatted.strip() + + +@pipeline +def langgraph_agent_pipeline() -> str: + """ZenML pipeline that orchestrates the LangGraph ReAct agent. + + Returns: + Formatted agent response + """ + # External artifact for agent query + agent_query = ExternalArtifact( + value="What is the weather in San Francisco?" + ) + + # Run the LangGraph agent + agent_results = run_langgraph_agent(agent_query) + + # Format the results + summary = format_langgraph_response(agent_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running LangGraph ReAct agent pipeline...") + run_result = langgraph_agent_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/llama_index/README.md b/examples/agent_framework_integrations/llama_index/README.md new file mode 100644 index 00000000000..c4c44bb61c6 --- /dev/null +++ b/examples/agent_framework_integrations/llama_index/README.md @@ -0,0 +1,16 @@ +# LlamaIndex + ZenML + +LlamaIndex Function Agent integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Function agents with async execution +- Multiple tool integration (weather, tip calculator) +- Async handling within ZenML steps \ No newline at end of file diff --git a/examples/agent_frameworks/llama_index/requirements.txt b/examples/agent_framework_integrations/llama_index/requirements.txt similarity index 100% rename from examples/agent_frameworks/llama_index/requirements.txt rename to examples/agent_framework_integrations/llama_index/requirements.txt diff --git a/examples/agent_framework_integrations/llama_index/run.py b/examples/agent_framework_integrations/llama_index/run.py new file mode 100644 index 00000000000..d94189e3385 --- /dev/null +++ b/examples/agent_framework_integrations/llama_index/run.py @@ -0,0 +1,106 @@ +"""ZenML Pipeline for LlamaIndex Function Agent. + +This pipeline demonstrates how to integrate LlamaIndex agents with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from agent import agent + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_llamaindex_agent( + query: str, +) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the LlamaIndex Function Agent and return results.""" + + async def run_agent_async(): + return await agent.run(query) + + try: + # LlamaIndex agent.run() is actually async and needs proper await + import asyncio + + # Create and run in event loop + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + response = loop.run_until_complete(run_agent_async()) + finally: + loop.close() + + # Extract the response content + if hasattr(response, "response"): + result = str(response.response) + else: + result = str(response) + + return {"query": query, "response": result, "status": "success"} + except Exception as e: + return { + "query": query, + "response": f"Agent error: {str(e)}", + "status": "error", + } + + +@step +def format_llamaindex_response( + agent_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the LlamaIndex agent results into a readable summary.""" + query = agent_data["query"] + response = agent_data["response"] + status = agent_data["status"] + + if status == "error": + formatted = f"""❌ LLAMAINDEX AGENT ERROR +{"=" * 40} + +Query: {query} +Error: {response} +""" + else: + formatted = f"""πŸ¦™ LLAMAINDEX FUNCTION AGENT RESPONSE +{"=" * 40} + +Query: {query} + +Response: +{response} + +πŸ”§ Powered by LlamaIndex (Function Agent + Tools) +""" + + return formatted.strip() + + +@pipeline +def llamaindex_agent_pipeline() -> str: + """ZenML pipeline that orchestrates the LlamaIndex Function Agent. + + Returns: + Formatted agent response + """ + # External artifact for agent query + agent_query = ExternalArtifact( + value="What's the weather in New York and calculate a tip for a $50 bill?" + ) + + # Run the LlamaIndex agent + agent_results = run_llamaindex_agent(agent_query) + + # Format the results + summary = format_llamaindex_response(agent_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running LlamaIndex Function Agent pipeline...") + run_result = llamaindex_agent_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/openai_agents_sdk/README.md b/examples/agent_framework_integrations/openai_agents_sdk/README.md new file mode 100644 index 00000000000..174515c8d62 --- /dev/null +++ b/examples/agent_framework_integrations/openai_agents_sdk/README.md @@ -0,0 +1,16 @@ +# OpenAI Agents SDK + ZenML + +OpenAI Agents SDK integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Structured agent execution with OpenAI API +- Function tools with `@function_tool` decorator +- Built-in tracing and monitoring \ No newline at end of file diff --git a/examples/agent_frameworks/openai_agents_sdk/requirements.txt b/examples/agent_framework_integrations/openai_agents_sdk/requirements.txt similarity index 100% rename from examples/agent_frameworks/openai_agents_sdk/requirements.txt rename to examples/agent_framework_integrations/openai_agents_sdk/requirements.txt diff --git a/examples/agent_framework_integrations/openai_agents_sdk/run.py b/examples/agent_framework_integrations/openai_agents_sdk/run.py new file mode 100644 index 00000000000..aee881d13f9 --- /dev/null +++ b/examples/agent_framework_integrations/openai_agents_sdk/run.py @@ -0,0 +1,90 @@ +"""ZenML Pipeline for OpenAI Agents SDK. + +This pipeline demonstrates how to integrate OpenAI Agents SDK with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from openai_agent import agent + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_openai_agent(query: str) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the OpenAI Agents SDK agent and return results.""" + try: + import agents + + # Use the default agent runner to execute the agent + runner = agents.run.DEFAULT_AGENT_RUNNER + result = runner.run_sync(agent, query) + + # Extract the final output + response = result.final_output + + return {"query": query, "response": response, "status": "success"} + except Exception as e: + return { + "query": query, + "response": f"Agent error: {str(e)}", + "status": "error", + } + + +@step +def format_openai_response( + agent_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the OpenAI Agents SDK results into a readable summary.""" + query = agent_data["query"] + response = agent_data["response"] + status = agent_data["status"] + + if status == "error": + formatted = f"""❌ OPENAI AGENTS SDK ERROR +{"=" * 40} + +Query: {query} +Error: {response} +""" + else: + formatted = f"""πŸ€– OPENAI AGENTS SDK RESPONSE +{"=" * 40} + +Query: {query} + +Response: +{response} + +πŸ”§ Powered by OpenAI Agents SDK (Tools + GPT) +""" + + return formatted.strip() + + +@pipeline +def openai_agent_pipeline() -> str: + """ZenML pipeline that orchestrates the OpenAI Agents SDK. + + Returns: + Formatted agent response + """ + # External artifact for agent query + agent_query = ExternalArtifact(value="Tell me a fun fact about Tokyo") + + # Run the OpenAI Agents SDK agent + agent_results = run_openai_agent(agent_query) + + # Format the results + summary = format_openai_response(agent_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running OpenAI Agents SDK pipeline...") + run_result = openai_agent_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/pydanticai/README.md b/examples/agent_framework_integrations/pydanticai/README.md new file mode 100644 index 00000000000..12ecac64d8d --- /dev/null +++ b/examples/agent_framework_integrations/pydanticai/README.md @@ -0,0 +1,16 @@ +# PydanticAI + ZenML + +PydanticAI type-safe agents integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Type-safe agent implementation +- Simple `run_sync()` API +- Built-in tool integration \ No newline at end of file diff --git a/examples/agent_frameworks/pydanticai/requirements.txt b/examples/agent_framework_integrations/pydanticai/requirements.txt similarity index 100% rename from examples/agent_frameworks/pydanticai/requirements.txt rename to examples/agent_framework_integrations/pydanticai/requirements.txt diff --git a/examples/agent_framework_integrations/pydanticai/run.py b/examples/agent_framework_integrations/pydanticai/run.py new file mode 100644 index 00000000000..26b14080768 --- /dev/null +++ b/examples/agent_framework_integrations/pydanticai/run.py @@ -0,0 +1,86 @@ +"""ZenML Pipeline for PydanticAI. + +This pipeline demonstrates how to integrate PydanticAI with ZenML +for orchestration and artifact management. +""" + +from typing import Annotated, Any, Dict + +from pydanticai_agent import agent + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_pydanticai_agent( + query: str, +) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the PydanticAI agent and return results.""" + try: + # Execute the PydanticAI agent + result = agent.run_sync(query) + + return {"query": query, "response": result.output, "status": "success"} + except Exception as e: + return { + "query": query, + "response": f"Agent error: {str(e)}", + "status": "error", + } + + +@step +def format_pydanticai_response( + agent_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the PydanticAI results into a readable summary.""" + query = agent_data["query"] + response = agent_data["response"] + status = agent_data["status"] + + if status == "error": + formatted = f"""❌ PYDANTICAI AGENT ERROR +{"=" * 40} + +Query: {query} +Error: {response} +""" + else: + formatted = f"""🐍 PYDANTICAI RESPONSE +{"=" * 40} + +Query: {query} + +Response: +{response} + +πŸ”§ Powered by PydanticAI (Type-Safe AI Agents) +""" + + return formatted.strip() + + +@pipeline +def pydanticai_agent_pipeline() -> str: + """ZenML pipeline that orchestrates the PydanticAI agent. + + Returns: + Formatted agent response + """ + # External artifact for agent query + agent_query = ExternalArtifact(value="What is the secret data?") + + # Run the PydanticAI agent + agent_results = run_pydanticai_agent(agent_query) + + # Format the results + summary = format_pydanticai_response(agent_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running PydanticAI pipeline...") + run_result = pydanticai_agent_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_framework_integrations/semantic-kernel/README.md b/examples/agent_framework_integrations/semantic-kernel/README.md new file mode 100644 index 00000000000..98b09fc2a5a --- /dev/null +++ b/examples/agent_framework_integrations/semantic-kernel/README.md @@ -0,0 +1,16 @@ +# Semantic Kernel + ZenML + +Microsoft Semantic Kernel integrated with ZenML. + +## Run +```bash +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt +python run.py +``` + +## Features +- Plugin-based architecture with `@kernel_function` +- Automatic function calling capabilities +- Async chat completion with OpenAI integration \ No newline at end of file diff --git a/examples/agent_frameworks/semantic-kernel/requirements.txt b/examples/agent_framework_integrations/semantic-kernel/requirements.txt similarity index 100% rename from examples/agent_frameworks/semantic-kernel/requirements.txt rename to examples/agent_framework_integrations/semantic-kernel/requirements.txt diff --git a/examples/agent_framework_integrations/semantic-kernel/run.py b/examples/agent_framework_integrations/semantic-kernel/run.py new file mode 100644 index 00000000000..c8fe517f7aa --- /dev/null +++ b/examples/agent_framework_integrations/semantic-kernel/run.py @@ -0,0 +1,122 @@ +"""ZenML Pipeline for Semantic Kernel. + +This pipeline demonstrates how to integrate Semantic Kernel with ZenML +for orchestration and artifact management. +""" + +import asyncio +from typing import Annotated, Any, Dict + +from semantic_kernel_agent import kernel + +from zenml import ExternalArtifact, pipeline, step + + +@step +def run_semantic_kernel_agent( + query: str, +) -> Annotated[Dict[str, Any], "agent_results"]: + """Execute the Semantic Kernel agent and return results.""" + try: + + async def run_kernel_async(): + from semantic_kernel.connectors.ai.function_choice_behavior import ( + FunctionChoiceBehavior, + ) + from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import ( + OpenAIChatPromptExecutionSettings, + ) + from semantic_kernel.contents.chat_history import ChatHistory + + # Get the chat service + chat_service = kernel.get_service("openai-chat") + + # Create chat history with user message + history = ChatHistory() + history.add_user_message(query) + + # Configure settings to enable function calling + settings = OpenAIChatPromptExecutionSettings() + settings.function_choice_behavior = FunctionChoiceBehavior.Auto() + + # Execute the chat with the kernel + response = await chat_service.get_chat_message_content( + chat_history=history, + settings=settings, + kernel=kernel, + ) + + return response.content + + # Run the async function + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + response = loop.run_until_complete(run_kernel_async()) + finally: + loop.close() + + return {"query": query, "response": response, "status": "success"} + except Exception as e: + return { + "query": query, + "response": f"Agent error: {str(e)}", + "status": "error", + } + + +@step +def format_semantic_kernel_response( + agent_data: Dict[str, Any], +) -> Annotated[str, "formatted_response"]: + """Format the Semantic Kernel results into a readable summary.""" + query = agent_data["query"] + response = agent_data["response"] + status = agent_data["status"] + + if status == "error": + formatted = f"""❌ SEMANTIC KERNEL AGENT ERROR +{"=" * 40} + +Query: {query} +Error: {response} +""" + else: + formatted = f"""🧠 SEMANTIC KERNEL RESPONSE +{"=" * 40} + +Query: {query} + +Response: +{response} + +πŸ”§ Powered by Microsoft Semantic Kernel (AI Orchestration) +""" + + return formatted.strip() + + +@pipeline +def semantic_kernel_agent_pipeline() -> str: + """ZenML pipeline that orchestrates the Semantic Kernel agent. + + Returns: + Formatted agent response + """ + # External artifact for agent query + agent_query = ExternalArtifact(value="What is the weather in Tokyo?") + + # Run the Semantic Kernel agent + agent_results = run_semantic_kernel_agent(agent_query) + + # Format the results + summary = format_semantic_kernel_response(agent_results) + + return summary + + +if __name__ == "__main__": + print("πŸš€ Running Semantic Kernel pipeline...") + run_result = semantic_kernel_agent_pipeline() + print("Pipeline completed successfully!") + print("Check the ZenML dashboard for detailed results and artifacts.") diff --git a/examples/agent_frameworks/README.md b/examples/agent_frameworks/README.md deleted file mode 100644 index 54b12034536..00000000000 --- a/examples/agent_frameworks/README.md +++ /dev/null @@ -1,143 +0,0 @@ -# Agent Frameworks Integration with ZenML - -This directory contains examples of integrating various agent frameworks with ZenML pipelines. Each example demonstrates how to zenml-ify agent systems while maintaining clean, consistent patterns. - -## πŸ“‹ Established Rules & Patterns - -Based on our experience integrating multiple agent frameworks, here are the key rules and best practices: - -### 1. πŸ”§ Environment Setup -- **Use `uv` for virtual environments**: `uv venv --python 3.11` in each folder -- **Use `uv` for package installation**: `uv pip install -r requirements.txt` -- **Install ZenML integrations**: `zenml integration install s3 aws -y --uv` (when needed) - -### 2. 🎯 Input Handling -- **Use ExternalArtifact for inputs**: Instead of creating input steps, use `ExternalArtifact(value="query")` to pass data directly into pipelines -- **Simple string inputs**: Prefer natural language strings over complex Pydantic models for user inputs -- **Single test case**: Focus on one example per pipeline rather than loops over multiple inputs - -### 3. πŸ“¦ Artifact Management -- **Annotated artifacts**: ALL ZenML step outputs must use `Annotated[Type, "artifact_name"]` -- **Descriptive artifact names**: Use clear names like `"agent_results"`, `"formatted_response"`, `"travel_query"` -- **Consistent types**: Use appropriate Python types (str, Dict, List) with proper annotations - -### 4. πŸ—οΈ Pipeline Structure -Follow this three-step pattern for all agent integrations: - -```python -@pipeline -def agent_pipeline() -> str: - # 1. External artifact for input - query = ExternalArtifact(value="Your query here") - - # 2. Agent execution step - results = run_agent_system(query) - - # 3. Output formatting step - summary = format_results(results) - - return summary -``` - -### 5. πŸ”„ Agent Execution Patterns -- **Error handling**: Always wrap agent execution in try-catch blocks -- **Resource management**: Properly start/stop runtimes or connections -- **Async handling**: Use `asyncio.run()` for async agent frameworks within sync ZenML steps -- **API compatibility**: Check the correct methods for each framework (e.g., `agent()` vs `agent.run()`) - -### 6. πŸ“ Code Quality -- **Concise code**: No unnecessary bloat, focus on core functionality -- **Proper imports**: Use absolute imports and correct module paths -- **Type hints**: Include proper type annotations for all functions -- **Documentation**: Clear docstrings explaining what each step does - -### 7. ⚑ Testing & Execution -- **Single execution**: Test with one example rather than loops -- **Pipeline validation**: Ensure each step executes successfully -- **Artifact verification**: Check that artifacts are properly stored and retrievable -- **Performance**: Aim for reasonable execution times (under 30 seconds for examples) - -## πŸš€ Implemented Examples - -### Microsoft Autogen (`autogen/`) -- **Multi-agent coordination**: Weather + attraction specialist agents -- **Async runtime management**: Proper setup and teardown of agent runtime -- **Complex data flow**: Structured travel planning with multiple data sources - -### AWS Strands (`aws-strands/`) -- **Simple tool integration**: Weather checking tool with agent -- **Direct execution**: Callable agent interface (`agent(query)`) -- **Streamlined workflow**: Single agent, single tool, focused task - -### CrewAI (`crewai/`) -- **Multi-agent crews**: Weather checker + travel advisor working together -- **Task coordination**: Sequential task execution with context passing -- **Parameter handling**: Proper `inputs` dictionary for crew kickoff - -### Google ADK (`google-adk/`) -- **Tool integration**: Weather and time checking capabilities with real Google ADK -- **Gemini model**: Uses gemini-1.5-flash-latest for agent responses -- **Multi-tool agent**: Single agent with multiple tool capabilities - -## πŸ“‹ Implementation Checklist - -When adding a new agent framework: - -- [ ] Create `uv venv --python 3.11` environment -- [ ] Install requirements with `uv pip install -r requirements.txt` -- [ ] Install ZenML integrations as needed -- [ ] Create `pipeline.py` with three-step structure -- [ ] Use `ExternalArtifact` for input data -- [ ] Annotate all step outputs properly -- [ ] Add comprehensive error handling -- [ ] Test with single example execution -- [ ] Verify artifacts are stored correctly -- [ ] Update this README with framework-specific notes - -## πŸ” Framework-Specific Notes - -### Autogen -- Requires async runtime setup and proper agent registration -- Use `await Agent.register()` for agent registration -- Handle message serialization properly -- Resource cleanup is critical (runtime start/stop) - -### AWS Strands -- Agent is callable directly: `agent(query)` -- Simple tool integration with `@tool` decorator -- Streaming responses need `str()` conversion - -### CrewAI -- Use `crew.kickoff(inputs={"param": "value"})` for execution -- Set reasonable `max_iter` values to prevent timeouts -- Verbose mode can be disabled for cleaner logs -- Handle tool iteration limits gracefully - -### Google ADK -- Agent is callable directly: `agent(query)` -- Tools are passed as list during initialization -- Requires `google-genai` to be installed first to resolve dependencies -- Uses Gemini models for LLM capabilities - -## 🎯 Future Frameworks - -When adding new frameworks, follow the established patterns: -1. Study the framework's execution model -2. Identify the correct API methods -3. Handle async/sync execution properly -4. Implement proper error handling -5. Use consistent artifact naming -6. Test thoroughly with ZenML integration - -## πŸ“š Key Learnings - -1. **ExternalArtifact eliminates unnecessary steps**: Don't create steps just to pass data -2. **Agent frameworks vary significantly**: Each has different APIs and execution models -3. **Error handling is crucial**: Agent execution can fail in many ways -4. **Resource management matters**: Proper cleanup prevents resource leaks -5. **Simple is better**: Focus on core functionality rather than complex features -6. **ZenML caching works well**: Leverage caching for development efficiency - ---- - -*This documentation captures our experience integrating agent frameworks with ZenML. Update it as new patterns emerge or frameworks are added.* \ No newline at end of file diff --git a/examples/agent_frameworks/autogen/autogen_agent.py b/examples/agent_frameworks/autogen/autogen_agent.py deleted file mode 100644 index f37feb9b3ff..00000000000 --- a/examples/agent_frameworks/autogen/autogen_agent.py +++ /dev/null @@ -1,202 +0,0 @@ -""" -Multi-Agent Travel Assistant Example for PanAgent -================================================= - -This example demonstrates how to use Microsoft Autogen with PanAgent -to create a multi-agent travel planning system. Each agent specializes -in a different aspect of travel planning. -""" - -import asyncio -from typing import List - -from autogen_core import ( - AgentId, - AgentRuntime, - MessageContext, - RoutedAgent, - SingleThreadedAgentRuntime, - message_handler, -) -from pydantic import BaseModel - -# Message Types - Using Pydantic for better serialization support -# ================================================================ - - -class TravelQuery(BaseModel): - """User wants to plan a trip""" - - destination: str - days: int - - -class WeatherInfo(BaseModel): - """Weather information for a destination""" - - destination: str - forecast: str - temperature: str - - -class AttractionInfo(BaseModel): - """Tourist attractions for a destination""" - - destination: str - attractions: List[str] - - -class TravelPlan(BaseModel): - """Complete travel plan combining all information""" - - destination: str - days: int - weather: WeatherInfo - attractions: AttractionInfo - - -# Specialized Agents - Each one is an expert in their field -# ========================================================== - - -class WeatherAgent(RoutedAgent): - """Expert in weather information""" - - def __init__(self): - super().__init__("Weather Specialist") - - @message_handler - async def check_weather( - self, message: TravelQuery, ctx: MessageContext - ) -> WeatherInfo: - """Get weather for the destination""" - # Simulate weather API call - weather_data = { - "Paris": ("Partly cloudy with chance of rain", "15Β°C"), - "Tokyo": ("Clear and sunny", "22Β°C"), - "New York": ("Cloudy", "18Β°C"), - } - - forecast, temp = weather_data.get( - message.destination, ("Variable conditions", "20Β°C") - ) - - print( - f"🌀️ WeatherAgent: Checking weather for {message.destination}..." - ) - await asyncio.sleep(0.5) # Simulate API delay - - return WeatherInfo( - destination=message.destination, - forecast=forecast, - temperature=temp, - ) - - -class AttractionAgent(RoutedAgent): - """Expert in tourist attractions""" - - def __init__(self): - super().__init__("Tourism Specialist") - - @message_handler - async def find_attractions( - self, message: TravelQuery, ctx: MessageContext - ) -> AttractionInfo: - """Find top attractions for the destination""" - # Simulate attraction database - attractions_db = { - "Paris": [ - "Eiffel Tower", - "Louvre Museum", - "Notre-Dame", - "Champs-Γ‰lysΓ©es", - ], - "Tokyo": [ - "Tokyo Tower", - "Senso-ji Temple", - "Meiji Shrine", - "Shibuya Crossing", - ], - "New York": [ - "Statue of Liberty", - "Central Park", - "Times Square", - "Empire State Building", - ], - } - - attractions = attractions_db.get( - message.destination, - ["City Center", "Local Markets", "Historic District"], - ) - - print( - f"πŸ›οΈ AttractionAgent: Finding attractions in {message.destination}..." - ) - await asyncio.sleep(0.7) # Simulate database query - - return AttractionInfo( - destination=message.destination, - attractions=attractions[:3], # Top 3 attractions - ) - - -class TravelCoordinator(RoutedAgent): - """Coordinates between specialists to create a travel plan and returns it.""" - - def __init__(self): - super().__init__("Travel Coordinator") - - @message_handler - async def coordinate_travel_plan( - self, message: TravelQuery, _ - ) -> TravelPlan: - """ - Receives a query, fans out to specialist agents, gathers results, - and returns a complete travel plan. This is the main entrypoint - for an external request. - """ - print(f"πŸ“‹ Coordinator: Planning trip to {message.destination}") - - # Send requests to specialists concurrently - weather_task = self.send_message( - message, AgentId("weather_agent", "default") - ) - attraction_task = self.send_message( - message, AgentId("attraction_agent", "default") - ) - - # Await the results - weather_info, attraction_info = await asyncio.gather( - weather_task, attraction_task - ) - - # Assemble the final plan - travel_plan = TravelPlan( - destination=message.destination, - days=message.days, - weather=weather_info, - attractions=attraction_info, - ) - - print( - f"βœ… Coordinator: Complete travel plan created for {message.destination}" - ) - return travel_plan - - -async def setup_runtime() -> AgentRuntime: - """PanAgent entrypoint to configure and return the Autogen runtime.""" - runtime = SingleThreadedAgentRuntime() - - # Register all the agents that form the ensemble - await WeatherAgent.register(runtime, "weather_agent", lambda: WeatherAgent()) - await AttractionAgent.register( - runtime, "attraction_agent", lambda: AttractionAgent() - ) - await TravelCoordinator.register( - runtime, "coordinator", lambda: TravelCoordinator() - ) - - return runtime diff --git a/examples/agent_frameworks/aws-strands/agent.py b/examples/agent_frameworks/aws-strands/agent.py deleted file mode 100644 index fd5a4d2ead8..00000000000 --- a/examples/agent_frameworks/aws-strands/agent.py +++ /dev/null @@ -1,42 +0,0 @@ -import os # Added for env var lookup - -from strands import Agent, tool -from strands.models.openai import OpenAIModel - - -@tool -def get_weather(city: str) -> str: - """ - Very naive weather lookup that *always* returns a sunny forecast. - - Args: - city: Name of the city. - - Returns: - A short weather blurb. - """ - return f"It\u2019s always sunny in {city}!" - - -# Initialise the OpenAI model, pulling the key from the environment rather than -# hard-coding credentials. -model = OpenAIModel( - client_args={ - "api_key": os.getenv("OPENAI_API_KEY", ""), - }, - model_id="gpt-4o-mini", - params={"temperature": 0.7}, -) - - -# --------------------------------------------------------------------------- # -# Global agent # -# --------------------------------------------------------------------------- # -agent: Agent = Agent( - model=model, - tools=[get_weather], - system_prompt="You are a helpful assistant that can check weather for cities.", -) - -# NOTE: PanAgent discovers the `agent` variable at runtime; do **not** -# execute the agent from within this module. diff --git a/examples/agent_frameworks/crewai/crewai_agent.py b/examples/agent_frameworks/crewai/crewai_agent.py deleted file mode 100644 index d40200e028d..00000000000 --- a/examples/agent_frameworks/crewai/crewai_agent.py +++ /dev/null @@ -1,54 +0,0 @@ -from crewai import Agent, Crew, Task -from crewai.tools import tool - - -@tool("Weather Checker Tool") -def get_weather(city: str) -> str: - """Get weather for a given city.""" - return f"Current weather in {city}: Sunny, 22Β°C (72Β°F), light breeze" - - -# Weather Specialist Agent -weather_checker = Agent( - role="Weather Specialist", - goal="Check weather conditions for a given city: {city}", - backstory="You are a meteorology expert who provides accurate weather updates. When you get weather information, you immediately report it clearly.", - tools=[get_weather], - verbose=False, - max_iter=2, -) - - -# Travel Advisor Agent -travel_advisor = Agent( - role="Travel Advisor", - goal="Give practical travel advice based on weather conditions for {city}", - backstory="You are an experienced travel advisor who helps people prepare for their trips.", - verbose=False, - max_iter=2, -) - - -# Task 1: Check weather with parameterized city -check_weather_task = Task( - description="Check the current weather in {city} and provide a weather report.", - expected_output="A clear statement of the current weather conditions in {city} including temperature and conditions", - agent=weather_checker, -) - - -# Task 2: Packing advice based on weather -packing_advice_task = Task( - description="Based on the weather report for {city}, provide 3-5 specific items to pack for the trip.", - expected_output="A list of 3-5 specific items to pack based on the weather conditions", - agent=travel_advisor, - context=[check_weather_task], -) - - -# PanAgent will discover this 'crew' variable -crew = Crew( - agents=[weather_checker, travel_advisor], - tasks=[check_weather_task, packing_advice_task], - verbose=False, -) diff --git a/examples/agent_frameworks/google-adk/adk_agent.py b/examples/agent_frameworks/google-adk/adk_agent.py deleted file mode 100644 index 312b883c3eb..00000000000 --- a/examples/agent_frameworks/google-adk/adk_agent.py +++ /dev/null @@ -1,43 +0,0 @@ -import datetime -from zoneinfo import ZoneInfo - -from google.adk.agents import Agent - - -def get_weather(city: str) -> dict: - """Retrieves the current weather report for a specified city.""" - # This is a mock implementation for demonstration purposes - # In a real agent, you would integrate with a weather API - return { - "status": "success", - "report": f"The weather in {city} is sunny with a temperature of 22Β°C.", - "timestamp": datetime.datetime.now().isoformat(), - } - - -def get_current_time(timezone: str = "UTC") -> dict: - """Get the current time in the specified timezone.""" - try: - tz = ZoneInfo(timezone) - current_time = datetime.datetime.now(tz) - return { - "status": "success", - "time": current_time.strftime("%Y-%m-%d %H:%M:%S %Z"), - "timezone": timezone, - } - except Exception as e: - return { - "status": "error", - "message": f"Invalid timezone: {timezone}", - "error": str(e), - } - - -# PanAgent will discover this 'root_agent' variable -root_agent = Agent( - name="weather_time_agent", - model="gemini-1.5-flash-latest", - description="Agent to answer questions about the weather in a city and provide current time.", - instruction="You are a helpful agent who can answer user questions about the weather and current time. Use the available tools to provide accurate information.", - tools=[get_weather, get_current_time], -) diff --git a/examples/agent_frameworks/haystack/haystack_agent.py b/examples/agent_frameworks/haystack/haystack_agent.py deleted file mode 100644 index de1fed6d0d5..00000000000 --- a/examples/agent_frameworks/haystack/haystack_agent.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -Minimal Haystack RAG pipeline for PanAgent. - -How it works ------------- -1. We put three short documents into an `InMemoryDocumentStore`. -2. A BM25 retriever fetches relevant docs for the user's query. -3. A `PromptBuilder` renders a single template including those docs. -4. An `OpenAIChatGenerator` produces the final answer. - -PanAgent looks for a global variable called ``pipeline`` or ``agent``. Here we -expose ``pipeline`` so the Haystack adapter will auto-discover it. -""" - -from haystack import Document, Pipeline -from haystack.components.builders import PromptBuilder -from haystack.components.generators import OpenAIGenerator -from haystack.components.retrievers.in_memory import InMemoryBM25Retriever -from haystack.document_stores.in_memory import InMemoryDocumentStore - -# --------------------------------------------------------------------------- # -# 1. Create / populate a tiny in-memory document store # -# --------------------------------------------------------------------------- # -document_store = InMemoryDocumentStore() -document_store.write_documents( - [ - Document( - content="Paris is the capital of France and home to the Eiffel Tower." - ), - Document( - content="Berlin is the capital of Germany and famous for the Brandenburg Gate." - ), - Document( - content="Rome is the capital of Italy and known for the Colosseum." - ), - ] -) - -# --------------------------------------------------------------------------- # -# 2. Define pipeline components # -# --------------------------------------------------------------------------- # -retriever = InMemoryBM25Retriever(document_store=document_store) - -prompt_template = """ -Answer the question using ONLY the given documents. - -Documents: -{% for doc in documents %} - β€’ {{ doc.content }} -{% endfor %} - -Question: {{question}} - -Answer (concise): -""" - -prompt_builder = PromptBuilder(template=prompt_template) - -llm = OpenAIGenerator(model="gpt-4o-mini") - -# --------------------------------------------------------------------------- # -# 3. Assemble the pipeline # -# --------------------------------------------------------------------------- # -pipeline = Pipeline() -pipeline.add_component("retriever", retriever) -pipeline.add_component("prompt_builder", prompt_builder) -pipeline.add_component("llm", llm) - -# Connect retriever β†’ prompt_builder.documents and prompt_builder β†’ llm -pipeline.connect("retriever", "prompt_builder.documents") -pipeline.connect("prompt_builder", "llm") - -# Warm-up so the first request is fast -pipeline.warm_up() - -# --------------------------------------------------------------------------- # -# Example manual invocation (disabled so PanAgent remains the entry-point) # -# --------------------------------------------------------------------------- # -# if __name__ == "__main__": -# question = "What city is home to the Eiffel Tower?" -# result = pipeline.run( -# { -# "retriever": {"query": question}, -# "prompt_builder": {"question": question}, -# }, -# include_outputs_from={"llm"}, -# ) -# print(result["llm"]["replies"][0].content) diff --git a/examples/agent_frameworks/langchain/langchain_agent.py b/examples/agent_frameworks/langchain/langchain_agent.py deleted file mode 100644 index 8c0db3bbb3f..00000000000 --- a/examples/agent_frameworks/langchain/langchain_agent.py +++ /dev/null @@ -1,36 +0,0 @@ -from langchain_community.document_loaders import WebBaseLoader -from langchain_core.output_parsers import StrOutputParser -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.runnables import Runnable, RunnableLambda -from langchain_openai import ChatOpenAI - - -def load_docs(input_dict: dict) -> str: - """Load docs from a URL provided in the input and return the text content.""" - url = input_dict.get("url") - if not url: - return "No URL provided." - - try: - loader = WebBaseLoader(url) - docs = loader.load() - return "\n".join([doc.page_content for doc in docs]) - except Exception as e: - return f"Error loading URL: {str(e)}" - - -# Define the model and prompt -llm = ChatOpenAI(model="gpt-4o-mini") -prompt = ChatPromptTemplate.from_template( - "Write a concise summary of the following text:\n\n{text}" -) - -# Create the runnable chain that PanAgent will discover -# The adapter will pass the full JSON body as the input dictionary -chain: Runnable = ( - RunnableLambda(load_docs) - | RunnableLambda(lambda text: {"text": text}) - | prompt - | llm - | StrOutputParser() -) diff --git a/examples/agent_frameworks/langgraph/langgraph_agent.py b/examples/agent_frameworks/langgraph/langgraph_agent.py deleted file mode 100644 index 57b332915d7..00000000000 --- a/examples/agent_frameworks/langgraph/langgraph_agent.py +++ /dev/null @@ -1,19 +0,0 @@ -from langgraph.prebuilt import create_react_agent - - -def get_weather(city: str) -> str: - """Get weather for a given city.""" - return f"It's always sunny in {city}!" - - -agent = create_react_agent( - model="openai:gpt-4o-mini", - tools=[get_weather], - prompt="You are a helpful assistant", -) - -# # Run the agent -# result = agent.invoke( -# {"messages": [{"role": "user", "content": "what is the weather in sf"}]} -# ) -# print(result) diff --git a/examples/agent_frameworks/langgraph/requirements.txt b/examples/agent_frameworks/langgraph/requirements.txt deleted file mode 100644 index 3614460b75b..00000000000 --- a/examples/agent_frameworks/langgraph/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -langgraph>=0.5.3 -zenml \ No newline at end of file diff --git a/examples/agent_frameworks/llama_index/agent.py b/examples/agent_frameworks/llama_index/agent.py deleted file mode 100644 index e8e8f2688d3..00000000000 --- a/examples/agent_frameworks/llama_index/agent.py +++ /dev/null @@ -1,28 +0,0 @@ -import os - -from llama_index.core.agent.workflow import FunctionAgent -from llama_index.llms.openai import OpenAI - - -# Define simple tools -def get_weather(city: str) -> str: - """Get weather for a given city.""" - return f"It's always sunny in {city}!" - - -def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> str: - """Calculate tip amount for a restaurant bill.""" - tip = bill_amount * (tip_percentage / 100) - total = bill_amount + tip - return f"Tip: ${tip:.2f}, Total: ${total:.2f}" - - -# Create the agent -agent = FunctionAgent( - tools=[get_weather, calculate_tip], - llm=OpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY")), - system_prompt=( - "You are a helpful assistant that can check weather for any city " - "and calculate restaurant tips on demand." - ), -) diff --git a/examples/agent_frameworks/openai_agents_sdk/openai_agent.py b/examples/agent_frameworks/openai_agents_sdk/openai_agent.py deleted file mode 100644 index 6ffc867267e..00000000000 --- a/examples/agent_frameworks/openai_agents_sdk/openai_agent.py +++ /dev/null @@ -1,20 +0,0 @@ -from agents import Agent - - -def get_city_info(city: str) -> str: - """Provides a fun fact about a given city.""" - if "paris" in city.lower(): - return "Paris is known as the 'City of Light'." - elif "tokyo" in city.lower(): - return "Tokyo is the most populous metropolitan area in the world." - else: - return f"Sorry, I don't have a fun fact for {city}." - - -# PanAgent's adapter will discover this 'agent' variable by convention. -agent = Agent( - name="FactBot", - instructions="You are a helpful assistant that provides fun facts about cities using your tools.", - tools=[get_city_info], - # model="gpt-4o-mini" # The SDK defaults to a suitable model. -) diff --git a/examples/agent_frameworks/pydanticai/my_tool.py b/examples/agent_frameworks/pydanticai/my_tool.py deleted file mode 100644 index a8f37c32562..00000000000 --- a/examples/agent_frameworks/pydanticai/my_tool.py +++ /dev/null @@ -1,2 +0,0 @@ -def get_secret_data() -> str: - return "something_secret" diff --git a/examples/agent_frameworks/pydanticai/pydanticai_agent.py b/examples/agent_frameworks/pydanticai/pydanticai_agent.py deleted file mode 100644 index 77e3768d65c..00000000000 --- a/examples/agent_frameworks/pydanticai/pydanticai_agent.py +++ /dev/null @@ -1,12 +0,0 @@ -from my_tool import get_secret_data -from pydantic_ai import Agent - -agent = Agent( - "openai:gpt-4o-mini", - system_prompt="Be concise, reply with one sentence.", - tools=[get_secret_data], -) - -# Test code removed - agent will be invoked via FastAPI -# result = agent.run_sync("What is the secret data?") -# print(result) diff --git a/examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py b/examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py deleted file mode 100644 index cf770a5777e..00000000000 --- a/examples/agent_frameworks/semantic-kernel/semantic_kernel_agent.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -semantic_kernel_agent.py -~~~~~~~~~~~~~~~~~~~~~~~~ - -Stage 1 Semantic Kernel example for PanAgent. - -Discovery contract: - * Global ``kernel`` variable (Semantic Kernel ``Kernel`` instance) - * Optional ``create_kernel()`` factory lets adapters rebuild kernels - with alternative services or settings. - -Safe-import rule: - No network calls or async work is executed at module import time. -""" - -from __future__ import annotations - -import asyncio -from typing import Annotated - -from semantic_kernel import Kernel -from semantic_kernel.connectors.ai.function_choice_behavior import ( - FunctionChoiceBehavior, -) -from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion -from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import ( - OpenAIChatPromptExecutionSettings, -) -from semantic_kernel.contents.chat_history import ChatHistory -from semantic_kernel.functions import kernel_function - - -# --------------------------------------------------------------------------- # -# Example native plugin # -# --------------------------------------------------------------------------- # -class WeatherPlugin: - """A very small native plugin with one callable function.""" - - @kernel_function( - name="get_weather", - description="Return a short weather report for the specified city.", - ) - def get_weather( - self, - city: Annotated[str, "The city to get weather for"], - ) -> str: - """ - Pretend to fetch weather information. - - In real scenarios you might call an external API here. - """ - return f"It's always sunny in {city}!" - - -# --------------------------------------------------------------------------- # -# Kernel factory # -# --------------------------------------------------------------------------- # -def create_kernel( - *, - model_id: str = "gpt-4o-mini", - service_id: str = "openai-chat", -) -> Kernel: - """ - Build and return a ready-to-use Semantic Kernel instance. - - Parameters - ---------- - model_id: - OpenAI model to register (default: ``gpt-4o-mini``). - service_id: - Service identifier used when registering the chat completion service. - """ - kernel = Kernel() - - # Register OpenAI chat completion service (no network call at import time) - kernel.add_service( - OpenAIChatCompletion(ai_model_id=model_id, service_id=service_id) - ) - - # Register our Weather plugin - kernel.add_plugin(WeatherPlugin(), plugin_name="Weather") - - return kernel - - -# --------------------------------------------------------------------------- # -# PanAgent discovery variable # -# --------------------------------------------------------------------------- # -# IMPORTANT: PanAgent looks for this global name when framework == "semantic-kernel" -kernel: Kernel = create_kernel() - -__all__ = ["kernel", "create_kernel", "WeatherPlugin"] - - -# --------------------------------------------------------------------------- # -# Interactive demo (run only when executed directly) # -# --------------------------------------------------------------------------- # -async def _demo() -> None: # pragma: no cover – illustrative only - """Simple interactive test of function-calling behaviour.""" - chat_service = kernel.get_service(OpenAIChatCompletion) # type: ignore[arg-type] - - # Chat history with a user request that triggers function calling - history = ChatHistory() - history.add_user_message("What is the weather in Paris?") - - # Enable automatic function choice so the model may call our plugin - settings = OpenAIChatPromptExecutionSettings() - settings.function_choice_behavior = FunctionChoiceBehavior.Auto() - - response = await chat_service.get_chat_message_content( # type: ignore[attr-defined] - chat_history=history, - settings=settings, - kernel=kernel, - ) - print(f"Assistant: {response.content}") # noqa: T201 – demo print - - -if __name__ == "__main__": # pragma: no cover - # Run the async demo when the script is executed directly - asyncio.run(_demo()) From db1e7c7cdb7ef8eaa1e19616bc08950166e99370 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sat, 23 Aug 2025 19:14:17 +0200 Subject: [PATCH 3/8] Update ZenML logo image path in README.md --- examples/agent_framework_integrations/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/agent_framework_integrations/README.md b/examples/agent_framework_integrations/README.md index d21515f2557..cc88c8dd6d6 100644 --- a/examples/agent_framework_integrations/README.md +++ b/examples/agent_framework_integrations/README.md @@ -1,7 +1,7 @@
- ZenML Logo + ZenML Logo

Agent Frameworks Integration Examples

From b74c6853ffb9025b51a2a36447bad0934babef6a Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sat, 23 Aug 2025 19:37:23 +0200 Subject: [PATCH 4/8] Add OPENAI_API_KEY export to all agent frameworks.- Set OPENAI_API_KEY for each agent framework.- Add export command to set the API key --- .../agent_framework_integrations/README.md | 34 ++----------------- .../autogen/README.md | 1 + .../aws-strands/README.md | 1 + .../crewai/README.md | 1 + .../google-adk/README.md | 1 + .../haystack/README.md | 1 + .../langchain/README.md | 1 + .../langgraph/README.md | 1 + .../llama_index/README.md | 1 + .../openai_agents_sdk/README.md | 1 + .../pydanticai/README.md | 1 + .../semantic-kernel/README.md | 1 + 12 files changed, 13 insertions(+), 32 deletions(-) diff --git a/examples/agent_framework_integrations/README.md b/examples/agent_framework_integrations/README.md index cc88c8dd6d6..cf1cf730fd2 100644 --- a/examples/agent_framework_integrations/README.md +++ b/examples/agent_framework_integrations/README.md @@ -41,6 +41,7 @@ This collection demonstrates how to integrate popular agent frameworks with ZenM Choose any framework and get started in minutes: ```bash +export OPENAI_API_KEY="your-api-key-here" cd framework-name/ uv venv --python 3.11 source .venv/bin/activate @@ -120,14 +121,6 @@ def agent_pipeline() -> str: - **LlamaIndex**: Function agents with async capabilities - **OpenAI Agents SDK**: Structured execution with OpenAI -## πŸ’‘ Key Learnings - -1. **ExternalArtifact simplifies inputs**: No need for input steps -2. **Framework APIs vary significantly**: Each requires specific execution patterns -3. **Async handling matters**: Some frameworks require event loop management -4. **Error handling is crucial**: Agent execution can fail in many ways -5. **Consistent patterns scale**: Following established patterns makes integration easier - ## πŸ”„ Implementation Notes ### Async Frameworks @@ -154,28 +147,6 @@ Each framework returns different response types: - **UV**: For fast package management - **OpenAI API Key**: Most examples use OpenAI (set `OPENAI_API_KEY`) -## πŸ› οΈ Development - -### Adding New Frameworks - -1. Create folder structure: - ``` - new-framework/ - β”œβ”€β”€ README.md - β”œβ”€β”€ requirements.txt - └── run.py - ``` - -2. Follow the three-step pipeline pattern -3. Add framework-specific notes to main README -4. Test with ZenML integration - -### Code Quality -- **Error handling**: Always wrap agent execution in try-catch -- **Resource management**: Properly start/stop runtimes -- **Artifact naming**: Use descriptive, consistent names -- **Documentation**: Clear, concise README per framework - ## πŸ†˜ Getting Help - πŸ’¬ [Join our Slack community](https://zenml.io/slack) @@ -210,9 +181,8 @@ ZenML is an extensible, open-source MLOps framework for creating production-read [Core Concepts]: https://docs.zenml.io/getting-started/core-concepts [LLMOps Guide]: https://docs.zenml.io/user-guides/llmops-guide [Documentation]: https://docs.zenml.io/ -[API Reference]: https://apidocs.zenml.io/ +[SDK Reference]: https://sdkdocs.zenml.io/ [Examples]: https://github.com/zenml-io/zenml/tree/main/examples -[Podcast]: https://podcast.zenml.io/ --- diff --git a/examples/agent_framework_integrations/autogen/README.md b/examples/agent_framework_integrations/autogen/README.md index 18dd09d83e1..68a89631051 100644 --- a/examples/agent_framework_integrations/autogen/README.md +++ b/examples/agent_framework_integrations/autogen/README.md @@ -4,6 +4,7 @@ Multi-agent conversation framework integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/aws-strands/README.md b/examples/agent_framework_integrations/aws-strands/README.md index d3a2f30bc1e..5c09699c9b4 100644 --- a/examples/agent_framework_integrations/aws-strands/README.md +++ b/examples/agent_framework_integrations/aws-strands/README.md @@ -4,6 +4,7 @@ AWS Strands agent framework integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/crewai/README.md b/examples/agent_framework_integrations/crewai/README.md index fb4b8cbd1ca..cbab0df9f09 100644 --- a/examples/agent_framework_integrations/crewai/README.md +++ b/examples/agent_framework_integrations/crewai/README.md @@ -4,6 +4,7 @@ CrewAI multi-agent crew framework integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/google-adk/README.md b/examples/agent_framework_integrations/google-adk/README.md index 6380e07778a..ddf79c4c8a2 100644 --- a/examples/agent_framework_integrations/google-adk/README.md +++ b/examples/agent_framework_integrations/google-adk/README.md @@ -4,6 +4,7 @@ Google Agent Development Kit integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/haystack/README.md b/examples/agent_framework_integrations/haystack/README.md index dee77820ec3..486ebf4d94b 100644 --- a/examples/agent_framework_integrations/haystack/README.md +++ b/examples/agent_framework_integrations/haystack/README.md @@ -4,6 +4,7 @@ Haystack RAG pipeline integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/langchain/README.md b/examples/agent_framework_integrations/langchain/README.md index 356faf9c2c9..b842c669913 100644 --- a/examples/agent_framework_integrations/langchain/README.md +++ b/examples/agent_framework_integrations/langchain/README.md @@ -4,6 +4,7 @@ LangChain agent chain integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/langgraph/README.md b/examples/agent_framework_integrations/langgraph/README.md index 2dcf4e5bd1c..800abdeb554 100644 --- a/examples/agent_framework_integrations/langgraph/README.md +++ b/examples/agent_framework_integrations/langgraph/README.md @@ -4,6 +4,7 @@ LangGraph ReAct agent integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/llama_index/README.md b/examples/agent_framework_integrations/llama_index/README.md index c4c44bb61c6..c041c537d4a 100644 --- a/examples/agent_framework_integrations/llama_index/README.md +++ b/examples/agent_framework_integrations/llama_index/README.md @@ -4,6 +4,7 @@ LlamaIndex Function Agent integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/openai_agents_sdk/README.md b/examples/agent_framework_integrations/openai_agents_sdk/README.md index 174515c8d62..d4803e77b54 100644 --- a/examples/agent_framework_integrations/openai_agents_sdk/README.md +++ b/examples/agent_framework_integrations/openai_agents_sdk/README.md @@ -4,6 +4,7 @@ OpenAI Agents SDK integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/pydanticai/README.md b/examples/agent_framework_integrations/pydanticai/README.md index 12ecac64d8d..cf27cf8f1b6 100644 --- a/examples/agent_framework_integrations/pydanticai/README.md +++ b/examples/agent_framework_integrations/pydanticai/README.md @@ -4,6 +4,7 @@ PydanticAI type-safe agents integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt diff --git a/examples/agent_framework_integrations/semantic-kernel/README.md b/examples/agent_framework_integrations/semantic-kernel/README.md index 98b09fc2a5a..b3e945a632d 100644 --- a/examples/agent_framework_integrations/semantic-kernel/README.md +++ b/examples/agent_framework_integrations/semantic-kernel/README.md @@ -4,6 +4,7 @@ Microsoft Semantic Kernel integrated with ZenML. ## Run ```bash +export OPENAI_API_KEY="your-api-key-here" uv venv --python 3.11 source .venv/bin/activate uv pip install -r requirements.txt From 61b2a8997e95e7e0fba9bb370cefabd301355e37 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sat, 23 Aug 2025 19:56:50 +0200 Subject: [PATCH 5/8] Add production vs. demos explanation in README.md --- examples/agent_framework_integrations/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/examples/agent_framework_integrations/README.md b/examples/agent_framework_integrations/README.md index cf1cf730fd2..4e1fb214e05 100644 --- a/examples/agent_framework_integrations/README.md +++ b/examples/agent_framework_integrations/README.md @@ -123,6 +123,15 @@ def agent_pipeline() -> str: ## πŸ”„ Implementation Notes +### Production vs. Demos +**These examples demonstrate single-query execution for simplicity.** In production, ZenML's value comes from: +- **Batch processing**: Process hundreds/thousands of queries overnight +- **Agent evaluation**: Compare different frameworks on test datasets +- **Data pipelines**: Use agents to process document collections +- **A/B testing**: Systematic comparison of agent configurations + +For real-time serving, use FastAPI/Flask directly. Use ZenML for the operational layer. + ### Async Frameworks Some frameworks require async handling within ZenML steps: - **LlamaIndex**: `asyncio.run(agent.run(query))` From 28717af6418444f72ca6e9af944babf6847c5da4 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sun, 24 Aug 2025 11:41:25 +0200 Subject: [PATCH 6/8] Update user guide examples and table with new agent data --- docs/book/user-guide/README.md | 2 +- docs/book/user-guide/toc.md | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/book/user-guide/README.md b/docs/book/user-guide/README.md index 176d4c808ad..044e407064a 100644 --- a/docs/book/user-guide/README.md +++ b/docs/book/user-guide/README.md @@ -24,4 +24,4 @@ Complete end-to-end implementations that showcase ZenML in real-world scenarios. Focused code snippets and templates that address specific ML workflow challenges.\ [See all examples in GitHub β†’](https://github.com/zenml-io/zenml-projects) -
QuickstartBridging Local Development and Cloud Deploymentexample-01.pnghttps://github.com/zenml-io/zenml/blob/main/examples/quickstart
End-to-End Batch InferenceSupervised ML project built with the ZenML framework and its integration.example-02.pnghttps://github.com/zenml-io/zenml/tree/main/examples/e2e
Basic NLP with BERTBuild NLP models with production-ready ML pipeline frameworkexample-03.pnghttps://github.com/zenml-io/zenml/tree/main/examples/e2e_nlp
Computer Vision with YoloV8End-to-end computer vision pipeline with modular designexample-04.pnghttps://github.com/zenml-io/zenml-projects/tree/main/end-to-end-computer-vision
LLM FinetuningLLM fine-tuning pipeline with PEFT approachexample-05.pnghttps://github.com/zenml-io/zenml/tree/main/examples/llm_finetuning
Agent Architecture ComparisonCompare AI agents with LangGraph workflows, LiteLLM integration, and automatic visualizations.example-06.pnghttps://github.com/zenml-io/zenml/blob/main/examples/agent_comparison
Minimal Agent ProductionDocument analysis service with pipelines, evaluation, and a simple web UI.example-07.pnghttps://github.com/zenml-io/zenml/blob/main/examples/minimal_agent_production
+
QuickstartBridging Local Development and Cloud Deploymentexample-01.pnghttps://github.com/zenml-io/zenml/blob/main/examples/quickstart
End-to-End Batch InferenceSupervised ML project built with the ZenML framework and its integration.example-02.pnghttps://github.com/zenml-io/zenml/tree/main/examples/e2e
Agent Architecture ComparisonCompare AI agents with LangGraph workflows, LiteLLM integration, and automatic visualizations.example-06.pnghttps://github.com/zenml-io/zenml/blob/main/examples/agent_comparison
Agent Framework IntegrationsProduction-ready integrations for 11 popular agent frameworks including LangChain, CrewAI, AutoGen, and more.example-06.pnghttps://github.com/zenml-io/zenml/tree/main/examples/agent_framework_integrations
Minimal Agent ProductionDocument analysis service with pipelines, evaluation, and a simple web UI.example-07.pnghttps://github.com/zenml-io/zenml/blob/main/examples/minimal_agent_production
Basic NLP with BERTBuild NLP models with production-ready ML pipeline frameworkexample-03.pnghttps://github.com/zenml-io/zenml/tree/main/examples/e2e_nlp
Computer Vision with YoloV8End-to-end computer vision pipeline with modular designexample-04.pnghttps://github.com/zenml-io/zenml-projects/tree/main/end-to-end-computer-vision
LLM FinetuningLLM fine-tuning pipeline with PEFT approachexample-05.pnghttps://github.com/zenml-io/zenml/tree/main/examples/llm_finetuning
diff --git a/docs/book/user-guide/toc.md b/docs/book/user-guide/toc.md index c07e58abd76..f1414584cf9 100644 --- a/docs/book/user-guide/toc.md +++ b/docs/book/user-guide/toc.md @@ -76,8 +76,11 @@ ## Examples * [Quickstart](https://github.com/zenml-io/zenml/blob/main/examples/quickstart) +* [Agent Quickstart](https://github.com/zenml-io/zenml/tree/main/examples/minimal_agent_production) +* [Agent examples](https://github.com/zenml-io/zenml/tree/main/examples/agent_framework_integrations/) * [End-to-End Batch Inference](https://github.com/zenml-io/zenml/tree/main/examples/e2e) -* [Basic NLP with BERT](https://github.com/zenml-io/zenml/tree/main/examples/e2e_nlp) +* [Forecasting time-series prediction](https://github.com/zenml-io/zenml-projects/tree/main/floracasts) +* [ML classification](https://github.com/zenml-io/zenml-projects/tree/main/oncoclear) * [Computer Vision with YoloV8](https://github.com/zenml-io/zenml-projects/tree/main/end-to-end-computer-vision) -* [LLM Finetuning](https://github.com/zenml-io/zenml/tree/main/examples/llm_finetuning) +* [Deep research](https://github.com/zenml-io/zenml-projects/tree/main/deep_research) * [More Projects...](https://github.com/zenml-io/zenml-projects) From 8425a9ad90393a5a69bddcfe39d01b934aa7a0c7 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Sun, 24 Aug 2025 21:07:38 +0200 Subject: [PATCH 7/8] Add new agent development guide with ZenML orchestration --- .../assets/agent-evaluation-overview.png | Bin 0 -> 163143 bytes .../assets/agent-monitoring-dashboard.png | Bin 0 -> 190486 bytes .../.gitbook/assets/agent-orchestration.png | Bin 0 -> 274032 bytes .../assets/agent-pipeline-pattern.png | Bin 0 -> 153935 bytes .../assets/agent-scaling-challenge.png | Bin 0 -> 187965 bytes .../.gitbook/assets/batch-optimization.png | Bin 0 -> 193707 bytes .../.gitbook/assets/batch-processing-flow.png | Bin 0 -> 166215 bytes .../assets/batch-processing-scale.png | Bin 0 -> 134333 bytes 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    • -# πŸ€– Agent Frameworks + ZenML +# πŸ€– ZenML Agent Development Platform -This collection demonstrates how to integrate popular agent frameworks with ZenML for production-grade AI agent orchestration. Each example follows consistent patterns and best practices, making it easy to adapt any framework for your specific use case. +This collection demonstrates how to use ZenML as your **agent development platform** - applying the same systematic development practices you use for traditional ML to agent development. Each example shows how any agent framework can benefit from ZenML's experiment tracking, evaluation workflows, and deployment capabilities. ## πŸš€ Quick Start @@ -121,17 +121,6 @@ def agent_pipeline() -> str: - **LlamaIndex**: Function agents with async capabilities - **OpenAI Agents SDK**: Structured execution with OpenAI -## πŸ”„ Implementation Notes - -### Production vs. Demos -**These examples demonstrate single-query execution for simplicity.** In production, ZenML's value comes from: -- **Batch processing**: Process hundreds/thousands of queries overnight -- **Agent evaluation**: Compare different frameworks on test datasets -- **Data pipelines**: Use agents to process document collections -- **A/B testing**: Systematic comparison of agent configurations - -For real-time serving, use FastAPI/Flask directly. Use ZenML for the operational layer. - ### Async Frameworks Some frameworks require async handling within ZenML steps: - **LlamaIndex**: `asyncio.run(agent.run(query))` @@ -163,36 +152,26 @@ Each framework returns different response types: - πŸ› [Report issues](https://github.com/zenml-io/zenml/issues) - πŸ’‘ [Request features](https://zenml.io/discussion) -## 🌟 About ZenML - -ZenML is an extensible, open-source MLOps framework for creating production-ready ML pipelines. These agent framework integrations showcase ZenML's flexibility in orchestrating AI workflows beyond traditional ML use cases. - -**Why ZenML for Agent Orchestration?** -- πŸ”„ **Reproducible workflows**: Version and track agent executions -- πŸ“Š **Artifact management**: Store and version agent inputs/outputs -- 🎯 **Production ready**: Built-in monitoring, logging, and error handling -- πŸ”§ **Tool agnostic**: Works with any agent framework -- ☁️ **Cloud native**: Deploy anywhere with consistent behavior - ## πŸ“– Learn More | Resource | Description | |----------|-------------| +| πŸ€– **[Agent Guide]** | Complete guide to agent development with ZenML | +| πŸ“Š **[LLM Evaluation]** | Proven evaluation patterns for agents and LLMs | | 🧘 **[ZenML 101]** | New to ZenML? Start here! | | βš› **[Core Concepts]** | Understand ZenML fundamentals | -| πŸ€– **[LLMOps Guide]** | Complete guide to LLMOps with ZenML | | πŸ““ **[Documentation]** | Full ZenML documentation | | πŸ“’ **[API Reference]** | Detailed API documentation | | ⚽ **[Examples]** | More ZenML examples | -| πŸ”ˆ **[Podcast]** | ML conversations every 2 weeks | -[ZenML 101]: https://docs.zenml.io/user-guides/starter-guide +[Agent Guide]: https://docs.zenml.io/user-guide/agent-guide +[LLM Evaluation]: https://docs.zenml.io/user-guide/llmops-guide/evaluation +[ZenML 101]: https://docs.zenml.io/user-guide/starter-guide [Core Concepts]: https://docs.zenml.io/getting-started/core-concepts -[LLMOps Guide]: https://docs.zenml.io/user-guides/llmops-guide [Documentation]: https://docs.zenml.io/ -[SDK Reference]: https://sdkdocs.zenml.io/ +[API Reference]: https://sdkdocs.zenml.io/ [Examples]: https://github.com/zenml-io/zenml/tree/main/examples --- -*This collection demonstrates the power and flexibility of ZenML for orchestrating diverse agent frameworks in production environments.* \ No newline at end of file +*This collection demonstrates how ZenML transforms agent development into a systematic, trackable workflow - applying the same rigor you use for traditional ML to agent development.* \ No newline at end of file From 3cc2781969b38cf2e513ee5a0704ae024420cab6 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 25 Aug 2025 22:20:46 +0200 Subject: [PATCH 8/8] Improve README.md with ZenML integration details --- docs/book/user-guide/agent-guide/README.md | 16 +++++----------- .../user-guide/agent-guide/agent-fundamentals.md | 14 ++++++++++++-- 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/docs/book/user-guide/agent-guide/README.md b/docs/book/user-guide/agent-guide/README.md index 15c37872847..6a35758f310 100644 --- a/docs/book/user-guide/agent-guide/README.md +++ b/docs/book/user-guide/agent-guide/README.md @@ -11,7 +11,9 @@ Transform your agent development from experimental scripts into systematic, prod ## Why pipelines for agents (for ML/AI engineers) -Agents evolve quickly: prompts change, tools are added/removed, and behavior shifts in production. A pipeline-first approach gives you reproducibility, lineage, and safe iteration: you version prompts and configs, deploy with confidence, and evaluate continuously on real traces. ZenML lets you apply the same rigor you use for classical ML to agentsβ€”regardless of whether you use an agent framework or direct LLM calls. The benefit is speed with control: faster iteration loops, clear governance, and a shared operating model across teams. +Agents evolve quickly: prompts change, tools are added/removed, and behavior shifts in production. A pipeline-first approach gives you reproducibility, lineage, and safe iteration: you version prompts and configs, deploy with confidence, and evaluate continuously on real traces. + +ZenML lets you apply the same rigor you use for classical ML to agentsβ€”regardless of whether you use an agent framework or direct LLM calls. The benefit is speed with control: faster iteration loops, clear governance, and a shared operating model across teams. Common pitfalls of ad‑hoc agent work today: - Experiment across notebooks and frameworks without shared structure @@ -21,7 +23,7 @@ Common pitfalls of ad‑hoc agent work today: **ZenML changes this** by applying the same systematic rigor you use for traditional ML to agent development. -Looking for a runnable starting point? See the end-to-end minimal example in `examples/minimal_agent_production`. +Looking for a runnable starting point? See the end-to-end minimal example in [`examples/minimal_agent_production`](https://github.com/zenml-io/zenml/tree/main/examples/minimal_agent_production). ## This Guide's Journey @@ -45,12 +47,4 @@ Each chapter builds on the previous one, creating a complete workflow from initi - Familiarity with [ZenML fundamentals](../starter-guide/) - Experience with [LLM evaluation patterns](../llmops-guide/evaluation/) (helpful but not required) -## Framework Philosophy - -**Important**: You don't need agent frameworks. Many successful production systems use direct LLM API calls. ZenML works with any approach - frameworks, custom code, or direct API calls. The key is systematic development, not the underlying implementation. - -Ready? Let's start with [development fundamentals](agent-fundamentals.md) to set up your systematic agent development workflow. - -{% hint style="info" %} -**Quick Start**: If you want to see working examples first, check our [framework integrations repository](https://github.com/zenml-io/zenml/tree/main/examples/agent_framework_integrations) with 11+ ready-to-run examples. -{% endhint %} \ No newline at end of file +Ready? Let's start with [development fundamentals](agent-fundamentals.md) to set up your systematic agent development workflow. \ No newline at end of file diff --git a/docs/book/user-guide/agent-guide/agent-fundamentals.md b/docs/book/user-guide/agent-guide/agent-fundamentals.md index 4d951c08c22..4891a67a67c 100644 --- a/docs/book/user-guide/agent-guide/agent-fundamentals.md +++ b/docs/book/user-guide/agent-guide/agent-fundamentals.md @@ -8,16 +8,26 @@ This chapter shows you how to transform experimental agent code into systematic,

    ZenML transforms agent development from experimental scripts into systematic, trackable workflows.

    +## A note on agent orchestrators vs ZenML + +Some of you might already be familiar with agent orchestration frameworks like [LangGraph](https://www.langchain.com/langgraph) or the [OpenAI SDK](https://openai.github.io/openai-agents-python/). The good news is that these frameworks integrate seamlessly with ZenML pipelines: you can easily embed agent workflows as steps within your pipeline, allowing you to orchestrate, track, and version your agent experiments alongside the rest of your ML workflow. This means you get the benefits of both worldsβ€”leveraging powerful agent frameworks while maintaining systematic experiment tracking and reproducibility through ZenML. + +Having said that, you don't need agent frameworks. Many successful production systems use direct LLM API calls. ZenML works with any approach - frameworks, custom code, or direct API calls. The key is systematic development, not the underlying implementation. + +{% hint style="info" %} +**Quick Start**: If you want to see working examples of ZenML with agent frameworks first, check our [framework integrations example](https://github.com/zenml-io/zenml/tree/main/examples/agent_framework_integrations) with 11+ ready-to-run examples. +{% endhint %} + ## From Scripts to Systematic Development Most agent development starts like this: ```python # Experimental notebook approach -agent_v1 = create_langchain_agent(prompt_template_v1) +agent_v1 = create_langgraph_agent(prompt_template_v1) agent_v2 = create_custom_agent(prompt_template_v2) -response_v1 = agent_v1.run("Analyze this customer feedback...") +response_v1 = agent_v1.invoke("Analyze this customer feedback...") response_v2 = agent_v2.run("Analyze this customer feedback...") # Manual comparison by eyeballing outputs