Merge remote-tracking branch 'upstream/main'

# Conflicts:
#	pyproject.toml
#	src/crewai/agent.py
#	src/crewai/crew.py
#	src/crewai/tools/tool_usage.py

README.md

@@ -82,7 +82,7 @@ researcher = Agent(
   verbose=True,
   allow_delegation=False,
   tools=[search_tool]
-  # You can pass an optional llm attribute specifying what mode you wanna use.
+  # You can pass an optional llm attribute specifying what model you wanna use.
   # It can be a local model through Ollama / LM Studio or a remote
   # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
   #
@@ -253,6 +253,7 @@ CrewAI uses anonymous telemetry to collect usage data with the main purpose of h
 There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
 
 Data collected includes:
+
 - Version of crewAI
   - So we can understand how many users are using the latest version
 - Version of Python

docs/how-to/LLM-Connections.md

@@ -16,8 +16,8 @@ The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. He
     - `role`: Defines the agent's role within the solution.
     - `goal`: Specifies the agent's objective.
     - `backstory`: Provides a background story to the agent.
-    - `llm`: The language model that will run the agent. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
-    - `function_calling_llm`: The language model that will handle the tool calling for this agent, overriding the crew function_calling_llm. Optional.
+    - `llm`: Indicates the Large Language Model the agent uses. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
+    - `function_calling_llm` *Optional*: Will turn the ReAct crewAI agent into a function calling agent.
     - `max_iter`: Maximum number of iterations for an agent to execute a task, default is 15.
     - `memory`: Enables the agent to retain information during and a across executions. Default is `False`.
     - `max_rpm`: Maximum number of requests per minute the agent's execution should respect. Optional.
@@ -42,7 +42,7 @@ example_agent = Agent(
 ```
 
 ## Ollama Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below. Note: Detailed Ollama setup is beyond this document's scope, but general guidance is provided.
+Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below. 
 
 ### Setting Up Ollama
 - **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
@@ -52,6 +52,70 @@ OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
 OPENAI_API_KEY=''
 ```
 
+## Ollama Integration (ex. for using Llama 2 locally)
+1. [Download Ollama](https://ollama.com/download).   
+2. After setting up the Ollama, Pull the Llama2 by typing following lines into the terminal ```ollama pull Llama2```.   
+3. Create a ModelFile similar the one below in your project directory.
+```
+FROM llama2
+
+# Set parameters
+
+PARAMETER temperature 0.8
+PARAMETER stop Result
+
+# Sets a custom system message to specify the behavior of the chat assistant
+
+# Leaving it blank for now.
+
+SYSTEM """"""
+```
+4. Create a script to get the base model, which in our case is llama2, and create a model on top of that with ModelFile above. PS: this will be ".sh" file.     
+```
+#!/bin/zsh
+
+# variables
+model_name="llama2"
+custom_model_name="crewai-llama2"
+
+#get the base model
+ollama pull $model_name
+
+#create the model file
+ollama create $custom_model_name -f ./Llama2ModelFile
+```
+5. Go into the directory where the script file and ModelFile is located and run the script.   
+6. Enjoy your free Llama2 model that powered up by excellent agents from crewai.   
+```
+from crewai import Agent, Task, Crew
+from langchain_openai import ChatOpenAI
+import os
+os.environ["OPENAI_API_KEY"] = "NA"
+
+llm = ChatOpenAI(
+    model = "crewai-llama2",
+    base_url = "http://localhost:11434/v1")
+
+general_agent = Agent(role = "Math Professor",
+                      goal = """Provide the solution to the students that are asking mathematical questions and give them the answer.""",
+                      backstory = """You are an excellent math professor that likes to solve math questions in a way that everyone can understand your solution""",
+                      allow_delegation = False,
+                      verbose = True,
+                      llm = llm)
+task = Task (description="""what is 3 + 5""",
+             agent = general_agent)
+
+crew = Crew(
+            agents=[general_agent],
+            tasks=[task],
+            verbose=2
+        )
+
+result = crew.kickoff()
+
+print(result)
+```
+
 ## HuggingFace Integration
 There are a couple of different ways you can use HuggingFace to host your LLM.
 

docs/tools/GitHubSearchTool.md

@@ -22,15 +22,15 @@ from crewai_tools import GithubSearchTool
 
 # Initialize the tool for semantic searches within a specific GitHub repository
 tool = GithubSearchTool(
-    github_repo='https://github.com/example/repo',
-    content_types=['code', 'issue'] # Options: code, repo, pr, issue
+	github_repo='https://github.com/example/repo',
+	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 
 # OR
 
 # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
 tool = GithubSearchTool(
-    content_types=['code', 'issue'] # Options: code, repo, pr, issue
+	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 ```
 

pyproject.toml

@@ -25,7 +25,7 @@ instructor = "^0.5.2"
 regex = "^2023.12.25"
 crewai-tools = { version = "^0.1.7", optional = true }
 click = "^8.1.7"
-python-dotenv = "1.0.0"
+python-dotenv = "^1.0.0"
 embedchain = "^0.1.98"
 appdirs = "^1.4.4"
 agentops = { version = "^0.1.6", optional = true }

src/crewai/tools/tool_usage.py

@@ -9,7 +9,11 @@ from crewai.agents.tools_handler import ToolsHandler
 from crewai.telemetry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.utilities import I18N, Converter, ConverterError, Printer
-import agentops
+agentops = None
+try:
+  import agentops
+except ImportError:
+  pass
 
 OPENAI_BIGGER_MODELS = ["gpt-4"]