WIP.

src/crewai/cli/templates/pipeline/config/agents.yaml

@@ -16,4 +16,19 @@ reporting_analyst:
   backstory: >
     You're a meticulous analyst with a keen eye for detail. You're known for
     your ability to turn complex data into clear and concise reports, making
-    it easy for others to understand and act on the information you provide.
+    it easy for others to understand and act on the information you provide.
+
+x_writer_agent:
+  role: >
+    Expert Social Media Content Creator specializing in short form written content
+  goal: >
+    Create viral-worthy, engaging short form posts that distill complex {topic} information
+    into compelling 280-character messages
+  backstory: >
+    You're a social media virtuoso with a particular talent for short form content. Your posts
+    consistently go viral due to your ability to craft hooks that stop users mid-scroll.
+    You've studied the techniques of social media masters like Justin Welsh, Dickie Bush,
+    Nicolas Cole, and Shaan Puri, incorporating their best practices into your own unique style.
+    Your superpower is taking intricate {topic} concepts and transforming them into
+    bite-sized, shareable content that resonates with a wide audience. You know exactly
+    how to structure a post for maximum impact and engagement.

src/crewai/cli/templates/pipeline/config/tasks.yaml

@@ -12,6 +12,28 @@ reporting_task:
     Review the context you got and expand each topic into a full section for a report.
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
-    A fully fledge reports with the mains topics, each with a full section of information.
+    A fully fledge reports with a title, mains topics, each with a full section of information.
-    Formatted as markdown without '```'
   agent: reporting_analyst
+
+write_x_task:
+  description: >
+    Using the research report provided, create an engaging short form post about {topic}.
+    Your post should have a great hook, summarize key points, and be structured for easy
+    consumption on a digital platform. The post must be under 280 characters.
+    Follow these guidelines:
+    1. Start with an attention-grabbing hook
+    2. Condense the main insights from the research
+    3. Use clear, concise language
+    4. Include a call-to-action or thought-provoking question if space allows
+    5. Ensure the post flows well and is easy to read quickly
+
+    Here is the title of the research report you will be using
+
+    Title: {title}
+    Research: 
+    {research}
+
+  expected_output: >
+    A compelling X post under 280 characters that effectively summarizes the key findings
+    about {topic}, starts with a strong hook, and is optimized for engagement on the platform.
+  agent: x_writer_agent

src/crewai/cli/templates/pipeline/crews/research_crew.py

@@ -0,0 +1,58 @@
+from pydantic import BaseModel
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+
+# Uncomment the following line to use an example of a custom tool
+# from {{folder_name}}.tools.custom_tool import MyCustomTool
+
+# Check our tools documentations for more information on how to use them
+# from crewai_tools import SerperDevTool
+
+
+class ResearchReport(BaseModel):
+	"""Research Report"""
+	title: str
+	body: str
+
+@CrewBase
+class ResearchCrew():
+	"""Research Crew"""
+	agents_config = '../config/agents.yaml'
+	tasks_config = '../config/tasks.yaml'
+
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			verbose=True
+		)
+
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
+
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+		)
+
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			output_pydantic=ResearchReport
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the Research Crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=2,
+		)

src/crewai/cli/templates/pipeline/crews/write_linkedin_crew.py

@@ -8,8 +8,8 @@ from crewai.project import CrewBase, agent, crew, task
 # from crewai_tools import SerperDevTool
 
 @CrewBase
-class {{crew_name}}Crew():
+class WriteLinkedInCrew():
-	"""{{crew_name}} crew"""
+	"""Research Crew"""
 	agents_config = '../config/agents.yaml'
 	tasks_config = '../config/tasks.yaml'
 
@@ -17,7 +17,6 @@ class {{crew_name}}Crew():
 	def researcher(self) -> Agent:
 		return Agent(
 			config=self.agents_config['researcher'],
-			# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
 			verbose=True
 		)
 
@@ -49,5 +48,4 @@ class {{crew_name}}Crew():
 			tasks=self.tasks, # Automatically created by the @task decorator
 			process=Process.sequential,
 			verbose=2,
-			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
 		)

src/crewai/cli/templates/pipeline/crews/write_x_crew.py

@@ -0,0 +1,37 @@
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+
+# Uncomment the following line to use an example of a custom tool
+# from {{folder_name}}.tools.custom_tool import MyCustomTool
+
+# Check our tools documentations for more information on how to use them
+# from crewai_tools import SerperDevTool
+
+@CrewBase
+class WriteXCrew():
+	"""Research Crew"""
+	agents_config = '../config/agents.yaml'
+	tasks_config = '../config/tasks.yaml'
+
+	@agent
+	def x_writer_agent(self) -> Agent:
+		return Agent(
+			config=self.agents_config['x_writer_agent'],
+			verbose=True
+		)
+	
+	@task
+	def write_x_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['write_x_task'],
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the Write X Crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=2,
+		)

src/crewai/cli/templates/pipeline/main.py

@@ -1,17 +1,26 @@
 #!/usr/bin/env python
 import asyncio
-from {{folder_name}}.src.{{folder_name}}.pipeline import {{pipeline_name}}Pipeline
+from pipeline import {{pipeline_name}}Pipeline
-
 
 async def run():
     """
     Run the pipeline.
     """
     inputs = [
-        {"topic": "AI LLMs"},
+        {"topic": "AI wearables"},
     ]
-    await {{pipeline_name}}Pipeline().pipeline().kickoff(inputs=inputs)
+    pipeline = {{pipeline_name}}Pipeline().pipeline()
+    results = await pipeline.kickoff(inputs)
+    
+    # Process and print results
+    for result in results:
+        print(f"Raw output: {result.raw}")
+        if result.json_dict:
+            print(f"JSON output: {result.json_dict}")
+        print("\n")
 
+def main():
+    asyncio.run(run())
 
 if __name__ == "__main__":
-    asyncio.run(run())
+    main()

src/crewai/cli/templates/pipeline/pipeline.py

@@ -1,26 +1,91 @@
+"""
+This pipeline file includes two different examples to demonstrate the flexibility of crewAI pipelines.
+
+Example 1: Two-Stage Pipeline
+-----------------------------
+This pipeline consists of two crews:
+1. ResearchCrew: Performs research on a given topic.
+2. WriteXCrew: Generates an X (Twitter) post based on the research findings.
+
+Key features:
+- The ResearchCrew's final task uses output_json to store all research findings in a JSON object.
+- This JSON object is then passed to the WriteXCrew, where tasks can access the research findings.
+
+Example 2: Three-Stage Pipeline with Parallel Execution
+-------------------------------------------------------
+This pipeline consists of three crews:
+1. ResearchCrew: Performs research on a given topic.
+2. WriteXCrew and WriteLinkedInCrew: Run in parallel, using the research findings to generate posts for X and LinkedIn, respectively.
+
+Key features:
+- Demonstrates the ability to run multiple crews in parallel.
+- Shows how to structure a pipeline with both sequential and parallel stages.
+
+Usage:
+- To switch between examples, comment/uncomment the respective code blocks below.
+- Ensure that you have implemented all necessary crew classes (ResearchCrew, WriteXCrew, WriteLinkedInCrew) before running.
+"""
+
+# Common imports for both examples
 from crewai import Pipeline
-from crewai.project import PipelineBase
+from crewai.project.pipeline_base import PipelineBase
-from crews.crew import *
+
+
+from crewai.project.annotations import pipeline
+
+# Uncomment the crews you need for your chosen example
+from .crews.research_crew import ResearchCrew
+from .crews.write_x_crew import WriteXCrew
+# from .crews.write_linkedin_crew import WriteLinkedInCrew  # Uncomment for Example 2
+
+# EXAMPLE 1: Two-Stage Pipeline
+# -----------------------------
+# Uncomment the following code block to use Example 1
 
 @PipelineBase
-class {{pipeline_name}}Pipeline:
+class ContentPipeline:
     def __init__(self):
         # Initialize crews
-        {% for crew_name in crew_names %}
+        self.research_crew = ResearchCrew().crew()
-        self.{{crew_name.lower()}}_crew = {{crew_name}}Crew().crew()
+        self.write_x_crew = WriteXCrew().crew()
-        {% endfor %}
+    
-
     @pipeline
     def create_pipeline(self):
         return Pipeline(
             stages=[
-                {% for crew_name in crew_names %}
+                self.research_crew,
-                self.{{crew_name.lower()}}_crew,
+                self.write_x_crew
-                {% endfor %}
             ]
         )
 
     async def run(self, inputs):
         pipeline = self.create_pipeline()
         results = await pipeline.kickoff(inputs)
-        return results
+        return results
+
+
+# EXAMPLE 2: Three-Stage Pipeline with Parallel Execution
+# -------------------------------------------------------
+# Uncomment the following code block to use Example 2
+
+# @PipelineBase
+# class ContentPipeline:
+#     def __init__(self):
+#         # Initialize crews
+#         self.research_crew = ResearchCrew().crew()
+#         self.write_x_crew = WriteXCrew().crew()
+#         self.write_linkedin_crew = WriteLinkedInCrew().crew()
+    
+#     @pipeline
+#     def create_pipeline(self):
+#         return Pipeline(
+#             stages=[
+#                 self.research_crew,
+#                 [self.write_x_crew, self.write_linkedin_crew]  # Parallel execution
+#             ]
+#         )
+
+#     async def run(self, inputs):
+#         pipeline = self.create_pipeline()
+#         results = await pipeline.kickoff(inputs)
+#         return results

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -6,14 +6,13 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.46.0" }
+#crewai = { extras = ["tools"], version = "^0.41.1" }
+crewai = { extras = ["tools"], path = "/Users/brandonhancock/Code/crewai/crewAI" }
+asyncio = "*"
 
 [tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:run"
+{{folder_name}} = "{{folder_name}}.src.{{folder_name}}.main:main"
-train = "{{folder_name}}.main:train"
-replay = "{{folder_name}}.main:replay"
-test = "{{folder_name}}.main:test"
 
 [build-system]
 requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"