first stab at early concepts

.gitignore

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+.DS_Store
+.pytest_cache
+__pycache__
+dist/

README.md

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-# CrewAI
+# CrewAI
+
+## Virtual Env
+```bash
+poetry shell
+```
+
+## Running Tests
+```bash
+poetry run pytest
+```
+
+## Packaging
+```bash
+poetry build
+```
+
+### Installing Locally
+```bash
+pip install dist/*.tar.gz
+```
+
+
+# CrewAI
+
+## Why?
+
+## How?
+
+## What is it?
+Convention?
+
+Convention of Roles
+Convention over Tools
+Convention over interactions (In what ways agents interact, how flexible, conversation patterns, where these happen [in a room, in isolation])
+Convention over degree of guidance
+
+You must be able to bring any tools <- Convention over tools
+
+How does it compare to autogen?
+
+Autogen is good to create conversational agents, these agents can then work 
+together autonomously, but there is no concepts of process, a process would need 
+to be programatically added.
+
+ChatDev brings the idea of processes to the AI Agents but it's stiff and it's
+customizations are not meant to be deployed at production settings
+
+CrewAI is a python library that provide modules to build crews of AI Agents tha
+
+What is the interface to interact with CrewAI?

crewai/__init__.py

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+from .crew import Crew
+from .agent import Agent

crewai/agent.py

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+"""Generic agent."""
+
+from typing import List, Any
+from pydantic import BaseModel, Field
+
+from langchain.tools import Tool
+from langchain.agents import AgentExecutor
+from langchain.chat_models import ChatOpenAI as OpenAI
+from langchain.tools.render import render_text_description
+from langchain.agents.format_scratchpad import format_log_to_str
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+
+from .prompts import AGENT_EXECUTION_PROMPT
+
+class Agent(BaseModel):
+	role: str = Field(description="Role of the agent")
+	goal: str = Field(description="Objective of the agent")
+	backstory: str = Field(description="Backstory of the agent")
+	tools: List[Tool] = Field(description="Tools at agents disposal")
+	llm: str = Field(description="LLM of the agent", default=OpenAI(
+		temperature=0.7,
+		model="gpt-4",
+		verbose=True
+	))
+  
+	def execute(self, task: str) -> str:
+		prompt = AGENT_EXECUTION_PROMPT.partial(
+			tools=render_text_description(self.tools),
+			tool_names=self.__tools_names(),
+			backstory=self.backstory,
+			role=self.role,
+			goal=self.goal,
+		)
+		return self.__run(task, prompt, self.tools)
+
+	def __run(self, input: str, prompt: str, tools: List[Tool]) -> str:
+		chat_with_bind = self.llm.bind(stop=["\nObservation"])
+		agent = {
+			"input": lambda x: x["input"],
+			"agent_scratchpad": lambda x: format_log_to_str(x['intermediate_steps'])
+		} | prompt | chat_with_bind | ReActSingleInputOutputParser()
+
+		agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
+		return agent_executor.invoke({"input": input})['output']
+
+	def __tools_names(self) -> str:
+		return ", ".join([t.name for t in self.tools])
+	

crewai/crew.py

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+"""Crew of agents."""
+
+from pydantic import BaseModel, Field
+
+class Crew(BaseModel):
+  description: str = Field(description="Description and of the crew being created.")
+  goal: str = Field(description="Objective of the crew being created.")

crewai/prompts.py

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+"""Prompts for generic agent."""
+
+from langchain.prompts import PromptTemplate
+
+AGENT_EXECUTION_PROMPT = PromptTemplate.from_template(
+"""You are {role}.
+{backstory}
+
+Your main goal is: {goal}
+
+TOOLS:
+------
+
+You have access to the following tools:
+
+{tools}
+
+To use a tool, please use the following format:
+
+```
+Thought: Do I need to use a tool? Yes
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+```
+
+When you have a response for your task, or if you do not need to use a tool, you MUST use the format:
+
+```
+Thought: Do I need to use a tool? No
+Final Answer: [your response here]
+```
+
+Begin!
+
+Current Task: {input}
+{agent_scratchpad}
+"""
+)

pyproject.toml

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+[tool.poetry]
+name = "crewai"
+version = "0.1.0"
+description = ""
+authors = ["Joao Moura <joaomdmoura@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+pydantic = "^2.4.2"
+langchain = "^0.0.314"
+openai = "^0.28.1"
+
+[tool.poetry.group.test.dependencies]
+pytest = "^7.4"
+pytest-vcr = "^1.0.2"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

tests/agent_test.py

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+"""Test Agent creation and execution basic functionality."""
+
+import pytest
+
+from langchain.chat_models import ChatOpenAI as OpenAI
+
+from ..crewai import Agent
+from ..crewai import prompts
+
+def test_agent_creation():
+	agent = Agent(
+		role="test role",
+		goal="test goal",
+		backstory="test backstory",
+		tools=[],
+	)
+
+	assert agent.role == "test role"
+	assert agent.goal == "test goal"
+	assert agent.backstory == "test backstory"
+	assert agent.tools == []
+
+def test_agent_default_value():
+	agent = Agent(
+		role="test role",
+		goal="test goal",
+		backstory="test backstory",
+		tools=[],
+	)
+
+	assert isinstance(agent.llm, OpenAI)
+	assert agent.llm.model_name == "gpt-4"
+	assert agent.llm.temperature == 0.7
+	assert agent.llm.verbose == True
+
+@pytest.mark.vcr()
+def test_agent_execution():
+	agent = Agent(
+		role="test role",
+		goal="test goal",
+		backstory="test backstory",
+		tools=[],
+	)
+
+	output = agent.execute("How much is 1 + 1?")
+	assert output == "2"

tests/cassettes/test_agent_execution.yaml

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+      Do I need to use a tool? Yes\nAction: the action to take, should be one of []\nAction
+      Input: the input to the action\nObservation: the result of the action\n```\n\nWhen
+      you have a response for your task, or if you do not need to use a tool, you
+      MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer:
+      [your response here]\n```\n\nBegin!\n\nCurrent Task: How much is 1 + 1?\n\n"}],
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