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.flake8

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+[flake8]
+max-line-length=88

.gitignore

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+# Editors
+.vscode/
+.idea/
+
+# Vagrant
+.vagrant/
+
+# Mac/OSX
+.DS_Store
+
+# Windows
+Thumbs.db
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.python-version
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+*.pyc
+
+scripts/

.readthedocs.yaml

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+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+    # You can also specify other tool versions:
+    # nodejs: "19"
+    # rust: "1.64"
+    # golang: "1.19"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#    - pdf
+#    - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+# python:
+#    install:
+#    - requirements: docs/requirements.txt

Makefile

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+.PHONY: all
+all: clean cov build
+
+.PHONY: clean
+clean:
+	rm -rf dist/
+
+.PHONY: test
+test:
+	poetry run pytest
+
+.PHONY: cov
+cov:
+	poetry run pytest --cov --cov-report=term-missing
+
+.PHONY: build
+build:
+	poetry build

README.md

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+# motleycrew
+A lightweight agent interaction framework.
+
+Minimal example with maximum automation (might take a while to build ;) ):
+
+```python
+from motleycrew import Task, MotleyCrew
+
+task = Task("""Research arxiv on the latest trends in machine learning
+and produce an engaging blog post on the topic""",
+            documents=["paper1.pdf", "paper2.pdf"])
+crew = MotleyCrew(tasks=[task])
+crew.run()
+```
+Come to think of it, might it make sense to make it 2 libraries, one with the interaction
+primitives, and the other on top of it with the automation?
+
+Here the MotleyCrew auto-spawns agents to complete the task, and picks additional tools for them.
+
+Short term, more crewAI-style (here some is copy-pasted from crewAI,
+will need to edit before going public)
+```python
+from langchain import hub
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_openai import ChatOpenAI
+from langchain.agents import AgentExecutor, create_openai_tools_agent
+
+from motley_crew import Task, MotleyCrew
+from motley_crew.agents import LangchainAgent, MotleyAgent
+
+tools=[DuckDuckGoSearchRun()]
+researcher_prompt = hub.pull("hwchase17/openai-tools-agent")
+llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
+
+researcher_agent = AgentExecutor(
+    agent=create_openai_tools_agent(llm, tools, researcher_prompt),
+    tools=tools,
+    verbose=True,
+)
+
+researcher = LangchainAgent(
+    agent=researcher_agent,
+    goal="Research the web and any documents they are given, and summarize the results",
+    allow_delegation=False
+)
+
+writer = MotleyAgent(
+    goal="Craft compelling content on tech advancements",
+    description="""You are a renowned Content Strategist, known for your insightful and engaging articles.
+  You transform complex concepts into compelling narratives.""",
+    verbose=True,
+    kind = "crewai",
+    delegation=True,
+)
+
+# Create tasks for your agents
+task1 = Task(
+    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+  Identify key trends, breakthrough technologies, and potential industry impacts.
+  Your final answer MUST be a full analysis report""",
+    agent=researcher,
+    documents = ["paper1.pdf", "paper2.pdf"],
+)
+
+task2 = Task(
+    description="""Using the insights provided, develop an engaging blog
+  post that highlights the most significant AI advancements.
+  Your post should be informative yet accessible, catering to a tech-savvy audience.
+  Make it sound cool, avoid complex words so it doesn't sound like AI.
+  Your final answer MUST be the full blog post of at least 4 paragraphs.
+  """,
+    agent=writer,
+    depends_on=task1,
+)
+
+# Instantiate your crew with a sequential process
+crew = MotleyCrew(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=2,  # You can set it to 1 or 2 to different logging levels
+)
+
+# Get your crew to work!
+result = crew.run()

examples/delegation_crewai.py

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+from dotenv import load_dotenv
+from langchain_community.tools import DuckDuckGoSearchRun
+
+from motleycrew import MotleyCrew, Task
+from motleycrew.agent.crewai import CrewAIMotleyAgent
+
+load_dotenv()
+
+search_tool = DuckDuckGoSearchRun()
+
+researcher = CrewAIMotleyAgent(
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You work at a leading tech think tank.
+Your expertise lies in identifying emerging trends.
+You have a knack for dissecting complex data and presenting actionable insights.""",
+    verbose=True,
+    delegation=False,
+    tools=[search_tool],
+)
+
+writer = CrewAIMotleyAgent(
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
+You transform complex concepts into compelling narratives.""",
+    verbose=True,
+    delegation=True,
+)
+
+# Create tasks for your agents
+crew = MotleyCrew()
+task1 = Task(
+    crew=crew,
+    name="produce comprehensive analysis report on AI advancements",
+    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+Identify key trends, breakthrough technologies, and potential industry impacts.
+Your final answer MUST be a full analysis report""",
+    agent=researcher,
+    documents=["paper1.pdf", "paper2.pdf"],  # will be ignored for now
+)
+
+task2 = Task(
+    crew=crew,
+    name="provide a literature summary of recent papers on AI",
+    description="""Conduct a comprehensive literature review of the latest advancements in AI in 2024.
+Identify key papers, researchers, and companies in the space.
+Your final answer MUST be a full literature review with citations""",
+    agent=researcher,
+)
+
+
+task3 = Task(
+    crew=crew,
+    name="produce blog post on AI advancements",
+    description="""Using the insights provided by a thorough web search, develop an engaging blog
+post that highlights the most significant AI advancements.
+Your post should be informative yet accessible, catering to a tech-savvy audience.
+Make it sound cool, avoid complex words so it doesn't sound like AI.
+Create a blog post of at least 4 paragraphs.""",
+    agent=writer,
+)
+
+[task1, task2] >> task3
+
+# Get your crew to work!
+result = crew.run(
+    agents=[researcher, writer],
+    verbose=2,  # You can set it to 1 or 2 to different logging levels
+)
+
+print(list(result._done)[0].outputs)

examples/math_crewai.py

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+from dotenv import load_dotenv
+
+from motleycrew import MotleyCrew, Task
+from motleycrew.agent.crewai import CrewAIMotleyAgent
+from motleycrew.tool.python_repl import create_repl_tool
+
+load_dotenv()
+
+repl_tool = create_repl_tool()
+
+# Define your agents with roles and goals
+solver1 = CrewAIMotleyAgent(
+    role="High School Math Teacher",
+    goal="Generate great solutions to math problems",
+    backstory="""You are a high school math teacher with a passion for problem-solving.
+To solve a math problem, you first reason about it, step by step, then generate the code to solve it exactly,
+using sympy, then use the REPL tool to evaluate that code, and then
+use the output to generate a human-readable solution.""",
+    verbose=True,
+    delegation=False,
+    tools=[repl_tool],
+)
+
+solver = solver1
+
+problems = [
+    "Problem: If $725x + 727y = 1500$ and $729x+ 731y = 1508$, what are the values of $x$, $y$, and $x - y$ ?",
+    "Drei Personen erhalten zusammen Fr. 2450. Die erste Person erh\u00e4lt Fr. 70 mehr als die zweite, aber Fr. 60 weniger als die dritte.\nWie viel erh\u00e4lt jede Person? ",
+    "Find all numbers $a$ for which the graph of $y=x^2+a$ and the graph of $y=ax$ intersect. Express your answer in interval notation.",
+]
+
+# Create tasks for your agents
+crew = MotleyCrew()
+task1 = Task(
+    crew=crew,
+    name="solve math problem",
+    description=f"""Create a nice human-readable solution to the following problem:
+    {problems[1]}
+     IN THE SAME LANGUAGE AS THE PROBLEM""",
+    agent=solver,
+)
+
+
+# Instantiate your crew with a sequential process
+result = crew.run(
+    agents=[solver],
+    verbose=True,  # You can set it to 1 or 2 to different logging levels
+)
+
+# Get your crew to work!
+print(list(result._done)[0].outputs)
+
+print("######################")

examples/single_crewai.py

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+from dotenv import load_dotenv
+from langchain_community.tools import DuckDuckGoSearchRun
+
+from motleycrew import MotleyCrew, Task
+from motleycrew.agent.crewai import CrewAIMotleyAgent
+
+load_dotenv()
+
+search_tool = DuckDuckGoSearchRun()
+
+researcher = CrewAIMotleyAgent(
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You work at a leading tech think tank.
+Your expertise lies in identifying emerging trends.
+You have a knack for dissecting complex data and presenting actionable insights.""",
+    verbose=True,
+    delegation=False,
+    tools=[search_tool],
+)
+
+
+# Create tasks for your agents
+crew = MotleyCrew()
+task1 = Task(
+    crew=crew,
+    name="produce comprehensive analysis report on AI advancements",
+    description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+  Identify key trends, breakthrough technologies, and potential industry impacts.
+  Your final answer MUST be a full analysis report""",
+    agent=researcher,
+    documents=["paper1.pdf", "paper2.pdf"],
+)
+
+# Instantiate your crew with a sequential process
+result = crew.run(
+    agents=[researcher],
+    verbose=2,  # You can set it to 1 or 2 to different logging levels
+)
+
+# Get your crew to work!
+print(list(result._done)[0].outputs)
+
+print("######################")