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devin/1737
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f2d3d03e22 | ||
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c642ebf97e | ||
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ea12543cee | ||
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a21e310d78 |
@@ -12,7 +12,7 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you
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To use the CrewAI CLI, make sure you have CrewAI installed:
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```shell
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```shell Terminal
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pip install crewai
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```
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@@ -20,7 +20,7 @@ pip install crewai
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The basic structure of a CrewAI CLI command is:
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```shell
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```shell Terminal
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crewai [COMMAND] [OPTIONS] [ARGUMENTS]
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```
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@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
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Create a new crew or flow.
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```shell
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```shell Terminal
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crewai create [OPTIONS] TYPE NAME
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```
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@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
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- `NAME`: Name of the crew or flow
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Example:
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```shell
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```shell Terminal
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crewai create crew my_new_crew
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crewai create flow my_new_flow
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```
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@@ -47,14 +47,14 @@ crewai create flow my_new_flow
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Show the installed version of CrewAI.
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```shell
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```shell Terminal
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crewai version [OPTIONS]
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```
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- `--tools`: (Optional) Show the installed version of CrewAI tools
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Example:
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```shell
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```shell Terminal
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crewai version
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crewai version --tools
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```
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@@ -63,7 +63,7 @@ crewai version --tools
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Train the crew for a specified number of iterations.
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```shell
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```shell Terminal
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crewai train [OPTIONS]
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```
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@@ -71,7 +71,7 @@ crewai train [OPTIONS]
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- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
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Example:
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```shell
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```shell Terminal
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crewai train -n 10 -f my_training_data.pkl
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```
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@@ -79,14 +79,14 @@ crewai train -n 10 -f my_training_data.pkl
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Replay the crew execution from a specific task.
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```shell
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```shell Terminal
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crewai replay [OPTIONS]
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```
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- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
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Example:
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```shell
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```shell Terminal
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crewai replay -t task_123456
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```
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@@ -94,7 +94,7 @@ crewai replay -t task_123456
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Retrieve your latest crew.kickoff() task outputs.
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```shell
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```shell Terminal
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crewai log-tasks-outputs
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```
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@@ -102,7 +102,7 @@ crewai log-tasks-outputs
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Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
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```shell
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```shell Terminal
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crewai reset-memories [OPTIONS]
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```
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@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
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- `-a, --all`: Reset ALL memories
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Example:
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```shell
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```shell Terminal
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crewai reset-memories --long --short
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crewai reset-memories --all
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```
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@@ -122,7 +122,7 @@ crewai reset-memories --all
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Test the crew and evaluate the results.
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```shell
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```shell Terminal
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crewai test [OPTIONS]
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```
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@@ -130,7 +130,7 @@ crewai test [OPTIONS]
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- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
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Example:
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```shell
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```shell Terminal
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crewai test -n 5 -m gpt-3.5-turbo
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```
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@@ -138,7 +138,7 @@ crewai test -n 5 -m gpt-3.5-turbo
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Run the crew.
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```shell
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```shell Terminal
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crewai run
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```
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<Note>
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@@ -147,7 +147,36 @@ Some commands may require additional configuration or setup within your project
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</Note>
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### 9. API Keys
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### 9. Chat
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Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
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After receiving the results, you can continue interacting with the assistant for further instructions or questions.
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```shell Terminal
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crewai chat
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```
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<Note>
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Ensure you execute these commands from your CrewAI project's root directory.
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</Note>
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<Note>
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IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
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```python
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@crew
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def crew(self) -> Crew:
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return Crew(
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agents=self.agents,
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tasks=self.tasks,
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process=Process.sequential,
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verbose=True,
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chat_llm="gpt-4o", # LLM for chat orchestration
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)
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```
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</Note>
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### 10. API Keys
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When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
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@@ -15,10 +15,48 @@ icon: wrench
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If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
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</Note>
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# Setting Up Your Environment
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Before installing CrewAI, it's recommended to set up a virtual environment. This helps isolate your project dependencies and avoid conflicts.
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<Steps>
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<Step title="Create a Virtual Environment">
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Choose your preferred method to create a virtual environment:
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**Using venv (Python's built-in tool):**
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```shell Terminal
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python3 -m venv .venv
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```
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**Using conda:**
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```shell Terminal
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conda create -n crewai-env python=3.12
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```
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</Step>
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<Step title="Activate the Virtual Environment">
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Activate your virtual environment based on your platform:
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**On macOS/Linux (venv):**
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```shell Terminal
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source .venv/bin/activate
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```
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**On Windows (venv):**
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```shell Terminal
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.venv\Scripts\activate
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```
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**Using conda (all platforms):**
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```shell Terminal
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conda activate crewai-env
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```
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</Step>
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</Steps>
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# Installing CrewAI
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CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently.
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Let's get you set up! 🚀
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Now let's get you set up! 🚀
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<Steps>
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<Step title="Install CrewAI">
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@@ -72,9 +110,9 @@ Let's get you set up! 🚀
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# Creating a New Project
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<Info>
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<Tip>
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We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
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</Info>
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</Tip>
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<Steps>
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<Step title="Generate Project Structure">
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@@ -101,10 +139,22 @@ Let's get you set up! 🚀
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│ └── __init__.py
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└── config/
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├── agents.yaml
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├── config.yaml
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└── tasks.yaml
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```
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</Frame>
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</Step>
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</Step>
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<Step title="Install Additional Tools">
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You can install additional tools using UV:
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```shell Terminal
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uv add <tool-name>
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```
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<Tip>
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UV is our preferred package manager as it's significantly faster than pip and provides better dependency resolution.
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</Tip>
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</Step>
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<Step title="Customize Your Project">
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Your project will contain these essential files:
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@@ -1,5 +1,5 @@
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---
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title: Composio
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title: Composio Tool
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description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
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icon: gear-code
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---
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@@ -75,7 +75,8 @@ filtered_action_enums = toolset.find_actions_by_use_case(
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)
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tools = toolset.get_tools(actions=filtered_action_enums)
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```<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
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```
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<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
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- Using specific tools:
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@@ -37,7 +37,6 @@ from crewai.tasks.task_output import TaskOutput
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from crewai.telemetry import Telemetry
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from crewai.tools.agent_tools.agent_tools import AgentTools
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from crewai.tools.base_tool import Tool
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from crewai.types.crew_chat import ChatInputs
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from crewai.types.usage_metrics import UsageMetrics
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from crewai.utilities import I18N, FileHandler, Logger, RPMController
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from crewai.utilities.constants import TRAINING_DATA_FILE
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@@ -84,6 +83,7 @@ class Crew(BaseModel):
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step_callback: Callback to be executed after each step for every agents execution.
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share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
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planning: Plan the crew execution and add the plan to the crew.
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chat_llm: The language model used for orchestrating chat interactions with the crew.
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"""
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__hash__ = object.__hash__ # type: ignore
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@@ -96,9 +96,9 @@ class TaskEvaluator:
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final_aggregated_data = ""
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for _, data in output_training_data.items():
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final_aggregated_data += (
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f"Initial Output:\n{data['initial_output']}\n\n"
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f"Human Feedback:\n{data['human_feedback']}\n\n"
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f"Improved Output:\n{data['improved_output']}\n\n"
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f"Initial Output:\n{data.get('initial_output', '')}\n\n"
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f"Human Feedback:\n{data.get('human_feedback', '')}\n\n"
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f"Improved Output:\n{data.get('improved_output', '')}\n\n"
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)
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evaluation_query = (
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Reference in New Issue
Block a user