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18 changes: 13 additions & 5 deletions README.md
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<div align="center">

![Logo of CrewAI, two people rowing on a boat](./docs/crewai_logo.png)
![Logo of CrewAI](./docs/crewai_logo.png)

# **CrewAI**

🤖 **CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
**CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.

**CrewAI Enterprise**
Want to plan, build (+ no code), deploy, monitor and interare your agents: [CrewAI Enterprise](https://www.crewai.com/enterprise). Designed for complex, real-world applications, our enterprise solution offers:

- **Seamless Integrations**
- **Scalable & Secure Deployment**
- **Actionable Insights**
- **24/7 Support**

<h3>

Expand Down Expand Up @@ -190,7 +198,7 @@ research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2024.
the current year is 2025.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
agent: researcher
Expand Down Expand Up @@ -392,7 +400,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
goal="Gather and validate supporting market data",
backstory="You excel at finding and correlating multiple data sources"
)

analysis_task = Task(
description="Analyze {sector} sector data for the past {timeframe}",
expected_output="Detailed market analysis with confidence score",
Expand All @@ -403,7 +411,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
expected_output="Corroborating evidence and potential contradictions",
agent=researcher
)

# Demonstrate crew autonomy
analysis_crew = Crew(
agents=[analyst, researcher],
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4 changes: 2 additions & 2 deletions docs/concepts/agents.mdx
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Expand Up @@ -43,7 +43,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
| **Max Retry Limit** _(optional)_ | `max_retry_limit` | `int` | Maximum number of retries when an error occurs. Default is 2. |
| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool` | Keep messages under context window size by summarizing. Default is True. |
| **Code Execution Mode** _(optional)_ | `code_execution_mode` | `Literal["safe", "unsafe"]` | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'. |
| **Embedder Config** _(optional)_ | `embedder_config` | `Optional[Dict[str, Any]]` | Configuration for the embedder used by the agent. |
| **Embedder** _(optional)_ | `embedder` | `Optional[Dict[str, Any]]` | Configuration for the embedder used by the agent. |
| **Knowledge Sources** _(optional)_ | `knowledge_sources` | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent. |
| **Use System Prompt** _(optional)_ | `use_system_prompt` | `Optional[bool]` | Whether to use system prompt (for o1 model support). Default is True. |

Expand Down Expand Up @@ -152,7 +152,7 @@ agent = Agent(
use_system_prompt=True, # Default: True
tools=[SerperDevTool()], # Optional: List of tools
knowledge_sources=None, # Optional: List of knowledge sources
embedder_config=None, # Optional: Custom embedder configuration
embedder=None, # Optional: Custom embedder configuration
system_template=None, # Optional: Custom system prompt template
prompt_template=None, # Optional: Custom prompt template
response_template=None, # Optional: Custom response template
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63 changes: 46 additions & 17 deletions docs/concepts/cli.mdx
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Expand Up @@ -12,15 +12,15 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you

To use the CrewAI CLI, make sure you have CrewAI installed:

```shell
```shell Terminal
pip install crewai
```

## Basic Usage

The basic structure of a CrewAI CLI command is:

```shell
```shell Terminal
crewai [COMMAND] [OPTIONS] [ARGUMENTS]
```

Expand All @@ -30,15 +30,15 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]

Create a new crew or flow.

```shell
```shell Terminal
crewai create [OPTIONS] TYPE NAME
```

- `TYPE`: Choose between "crew" or "flow"
- `NAME`: Name of the crew or flow

Example:
```shell
```shell Terminal
crewai create crew my_new_crew
crewai create flow my_new_flow
```
Expand All @@ -47,14 +47,14 @@ crewai create flow my_new_flow

Show the installed version of CrewAI.

```shell
```shell Terminal
crewai version [OPTIONS]
```

- `--tools`: (Optional) Show the installed version of CrewAI tools

Example:
```shell
```shell Terminal
crewai version
crewai version --tools
```
Expand All @@ -63,46 +63,46 @@ crewai version --tools

Train the crew for a specified number of iterations.

```shell
```shell Terminal
crewai train [OPTIONS]
```

- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")

Example:
```shell
```shell Terminal
crewai train -n 10 -f my_training_data.pkl
```

### 4. Replay

Replay the crew execution from a specific task.

```shell
```shell Terminal
crewai replay [OPTIONS]
```

- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks

Example:
```shell
```shell Terminal
crewai replay -t task_123456
```

### 5. Log-tasks-outputs

Retrieve your latest crew.kickoff() task outputs.

```shell
```shell Terminal
crewai log-tasks-outputs
```

### 6. Reset-memories

Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).

```shell
```shell Terminal
crewai reset-memories [OPTIONS]
```

Expand All @@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
- `-a, --all`: Reset ALL memories

Example:
```shell
```shell Terminal
crewai reset-memories --long --short
crewai reset-memories --all
```
Expand All @@ -122,23 +122,23 @@ crewai reset-memories --all

Test the crew and evaluate the results.

```shell
```shell Terminal
crewai test [OPTIONS]
```

- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")

Example:
```shell
```shell Terminal
crewai test -n 5 -m gpt-3.5-turbo
```

### 8. Run

Run the crew.

```shell
```shell Terminal
crewai run
```
<Note>
Expand All @@ -147,7 +147,36 @@ Some commands may require additional configuration or setup within your project
</Note>


### 9. API Keys
### 9. Chat

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.

After receiving the results, you can continue interacting with the assistant for further instructions or questions.

```shell Terminal
crewai chat
```
<Note>
Ensure you execute these commands from your CrewAI project's root directory.
</Note>
<Note>
IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.

```python
@crew
def crew(self) -> Crew:
return Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
chat_llm="gpt-4o", # LLM for chat orchestration
)
```
</Note>


### 10. API Keys

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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31 changes: 24 additions & 7 deletions docs/concepts/crews.mdx
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Expand Up @@ -23,14 +23,14 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Language** _(optional)_ | `language` | Language used for the crew, defaults to English. |
| **Language File** _(optional)_ | `language_file` | Path to the language file to be used for the crew. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |
| **Step Callback** _(optional)_ | `step_callback` | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
| **Task Callback** _(optional)_ | `task_callback` | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
| **Share Crew** _(optional)_ | `share_crew` | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
| **Output Log File** _(optional)_ | `output_log_file` | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
| **Output Log File** _(optional)_ | `output_log_file` | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`. |
| **Manager Agent** _(optional)_ | `manager_agent` | `manager` sets a custom agent that will be used as a manager. |
| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description. |
Expand Down Expand Up @@ -240,6 +240,23 @@ print(f"Tasks Output: {crew_output.tasks_output}")
print(f"Token Usage: {crew_output.token_usage}")
```

## Accessing Crew Logs

You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
In case of `True(Boolean)` will save as `logs.txt`.

In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.

```python Code
# Save crew logs
crew = Crew(output_log_file = True) # Logs will be saved as logs.txt
crew = Crew(output_log_file = file_name) # Logs will be saved as file_name.txt
crew = Crew(output_log_file = file_name.txt) # Logs will be saved as file_name.txt
crew = Crew(output_log_file = file_name.json) # Logs will be saved as file_name.json
```



## Memory Utilization

Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
Expand Down Expand Up @@ -279,9 +296,9 @@ print(result)
Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.

- `kickoff()`: Starts the execution process according to the defined process flow.
- `kickoff_for_each()`: Executes tasks for each agent individually.
- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
- `kickoff_async()`: Initiates the workflow asynchronously.
- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.

```python Code
# Start the crew's task execution
Expand Down
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